Aarup, M., Arentoft, M. M., Parrod, Y., Stadler, J., and Stokes, I. (1994). OPTIMUM-AIV: A knowledge-based planning and scheduling system for spacecraft AIV. In Fox, M. and Zweben, M. (Eds.), Knowledge Based Scheduling. Morgan K\aufmann, San Mateo, California.

Abramson, B. and Yung, M. (1989). Divide and conquer under global constraints: A solution to the N-queens problem. Journal of Parallel and Distributed Computing, 6(3), 649–662.

Ackley, D. H. and Littman, M. L. (1991). Interactions between learning and evolution. In Langton, C., Taylor, C. Farmer, J. D., and Ramussen, S. (Eds.) Artificial Life II, pp. 487–509. Addison-Wesley, Redwood City, California.

Aarup, M., Arentoft, M. M., Parrod, Y., Stadler, J., and Stokes, I. (1994). OPTIMUM-AIV: A knowledge-based planning and scheduling system for spacecraft AIV. In Fox, M. And zweben, M. (Eds.), Knowledge Based Scheduling.Morgan ractions between learning and evolution. In Langton, C., Taylor, C., Farmer, J. D., and Ramussen, S. (Eds.), Artificial Life II, pp. 487–509. Addison-Wesley, Redwood City, California.

Adelson-Velsky, G. M., Arlazarov, V. L., Bitman, A. R., Zhivotovsky, A. A., and Uskov, A. V. (1970). Programming a computer to play chess. Russian Mathematical Surveys, 25, 221–262.

Adelson-Velsky, G. M., Arlazarov, V. L., and Donskoy, M. V. (1975). Some methods of controlling the tree search in chess programs. Artificial Intelligence, 6(4), 361–371.

Agmon, S. (1954.) The relaxation method for linear inequalities. Canadian J. Math., 6(3), 382–392.

Agre, P. E. and Chapman, D. (1987). Pengi: an implementation of a theory of activity. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), pp. 268–72, Milan. Morgan Kaufman.

Aho, A. V., Hopcroft, J., and Ullman, J. D. (1974). The Design and Analysis of Computer Algorithms. Addison-Wesley, Reading, Massachusetts.

Aho, A. V. and Ullman, J. D. (1972). The Theory of Parsing, Translation and Compiling. Prentice-Hall, Upper Saddle River, New Jersey.

Ait-Kaci, H. and Podelski, A. (1993). Towards a meaning of LIFE. Journal of Logic Programming, 16(3–4), 195–234.

Aizerman, M., Braverman, E., and Rozonoer, L. (1964). Theoretical foundations of the potential functions method in pattern recognition learning. Automation and Remote Control, 25, 821–837.

Albus, J. S. (1975). A new approach to manipulator control: The cerebellar model articulation controller (CMAC). Journal of Dynamic Systems, Measurement, and Control, 97, 270–277.

Aldous, D. and Vazirani, U. (1994). „Go with the winners” algorithms. In Procceedings of the 35th Annual Symposiumon Foundations of Computer Science, pp. 492–501, Santa Fe, New Mexico. IEEE Computer Society Press.

Allais, M. (1953). Le comportment de l’homme rationnel devant la risque: critique des postulats et axiomes de l’école Américaine. Econometrica, 21, 503–546.

Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the Association for Computing Machinery, 26(11), 832–843.

Allen, J. F. (1984). Towards a general theory of action and time. Artificial Intelligence, 23, 123–154.

Allen, J. F. (1991). Time and time again: The many ways to represent time. International Journal of Intelligent Systems, 6, 341–355.

Allen, J. F. (1995). Natural Language Understanding. Benjamin/Cummings, Redwood City; California.

Allen, J. F., Hendler, J., and Tate, A. (Eds.). (1990). Readings in Planning. Morgan Kaufmann, San Mateo, California.

Almuallim, H. and Dietterich, T. (1991). Learning with many irrelevant features. In Proccedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Vol. 2, pp. 547–552, Anaheim, California. AAAI Press.

ALPAC (1966). Language and machines: Computers in translation and linguistics. Tech. rep. 1416, The Automatic Language Processing Advisory Committee of the National Academy of Sciences, Washington, DC.

Alshawi, H. (Ed.). (1992). The Core Language Engine. MIT Press, Cambridge, Massachusetts.

Alterman, R. (1988). Adaptive planning. Cognitive Science, 12, 393–422.

Amarel, F. (1998). On representations of problems of reasoning about actions. In Michie, D. (Ed.), Machine Intelligence 3, Vol. 3, pp. 131–171. Elsevier/North-Holland, Amsterdam, London, New York.

Ambros-Ingerson, J. and Steel, S. (1988). Integrating planning, execution and monitoring. In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), pp. 735–740, St. Paul, Minnesota. Morgan Kaufmann.

Amit, D., Gutfreund, H., and Sompolinsky, H. (1985). Spin-glass models of neural networks. Physical Review, A 32, 1007–1018.

Andersen, S. K., Olesen, K. G., Jensen, F. V., and Jensen F. (1989). HUGIN – a shell for building Bayesian belief universes for expert systems. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Vol. 2, pp. 1080–1085. Detroit. Morgan Kaufmann.

Anderson, A. R. (Ed.) 1964). Minds and Machines. Prentice-Hall, Upper Saddle River, New Jersey.

Anderson, J. A. and Rosenfeld, E. (Eds.). (1988). Neurocomputing: Foundations of Research. MIT Press, Cambridge, Massachusetts.

Anderson, J. R. (1980). Cognitive Psychology and Its Implications. W. H. Freeman, New York.

Anderson, J. R. (1993). The Architecture of Cognition. Harvard University Press, Cambridge, Massachusetts.

Andre, D. and Russell, S. J. (2002). State abstraction for programmable reinforcement learning agents. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp. 119–125, Edmonton, Alberta. AAAI Press.

Anshelevich, V. A. (2000). The game of Hex: An automatic theorem proving approach to game programming. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pp. 189–194. Austin, Texas. AAAI Press.

Anthony, M. and Bartlett, P. (1999). Neural Network Learning: Theoretical Foundations. Cambridge University Press, Cambridge, UK.

Appel, K. and Haken, W. (1977). Every planar map is four colorable: Part I: Discharging. Illinois J. Math., 21, 429–490.

Apt, K. R. (1999). The essence of constraint propagation. Theoretical Computer Science, 221 (1–2), 179–210.

Apté, C. Damerau, F., and Weiss, S. (1994). Automated learning of decision rules for text categorization. ACM Transactions on Information Systems, 12, 233–251.

Arkin, R. (1998). Behavior-Based Robotics. MIT Press, Boston, MA.

Armstrong, D. M. (1968). A Materialist Theory of the Mind. Routledge and Kegan Paul, London.

Arnauld, A. (1662). La logique, ou l’art de penser. Chez Charles Savreux, au pied de la Tour de Nostre Dame, Paris.

Arora, S. (1998). Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems. Journal of the Association for Computing Machinery, 45(5), 753–782.

Ashby, W. R. (1940). Adaptiveness and equilibrium. Journal of Mental Science, 86, 478–483.

Ashby, W. R. (1948). Design for a brain. Electronic Engineering, December, 379–383.

Ashby, W. R. (1952). Design for a Brain. Wiley, New York.

Asimov, I. (1942). Runaround. Astounding Science Fiction, March.

Asimov, I. (1950). I. Robot. Doubleday, Garden City, New York.

Astrom, K. J. (1965). Optimal control of Markov decision processes with incomplete state estimation. J. Math. Anal. Applic., 10, 174–205.

Audi, R. (Ed.). (1999). The Cambridge Dictionary of Philosophy. Cambridge University Press, Cambridge, UK.

Austin, J. L. (1962). How To Do Things with Words. Harvard University Press, Cambridge, Massachusetts.

Axelrod, R. (1985). The Evolution of Cooperation. Basic Books, New York.

Bacchus, F. (1990). Representing and Reasoning with Probabilistic Knowledge. MIT Press, Cambridge, Massachusetts.

Bacchus, F. and Grove, A. (1995). Graphical models for preference and utility. In Uncertainty in Artificial Intelligence: Proceedings of the Eleventh Conference, pp. 3–10, Montreal, Canada, Morgan Kaufmann.

Bacchus, F. and Grove, A. (1996). Utility independence in a qualitative decision theory. In Proceedings of the Fifth International Conference on the Principles of Knowledge Representation and Reasoning, pp. 542–552, San Mateo, California, Morgan Kaufmann.

Bacchus, F. Grove, A., Halpern, J. Y., and Koller, D. (1992). From statistics to beliefs. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 602–608, San Jose. AAAI Press.

Bacchus, F. and van Beek, P. (1998). On the conversion between non-binary and binary constraint satisfaction problems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 311–318, Madison, Wisconsin. AAAI Press.

Bacchus, F. and van Run, P. (1995). Dynamic variable ordering in CSPs. In Proceedings of the First International Conference on Principles and Practice of Constraint Programming, pp. 258–275, Cassis, France. Springer-Verlag.

Bachmann, P. G. H. (1894). Die analytische Zahlentheorie. B. G. Teubner, Leipzig.

Backus, J. W. (1996). Transcript of question and answer session. In Wexelblat, R. L. (Ed.), History of Programming Languages, p. 162. Academic Press, New York.

Baeza-Yates, R. and Ribeiro-Neto, B. (1999). Modern Information Retrieval. Addison Wesley Longman, Reading, Massachusetts.

Bajcsy, R. and Lieberman, L. (1976). Texture gradient as a depth cue. Computer Graphics and Image Processing, 5(1), 52–67.

Baker, C. L. (1989). English Syntax. MIT Press, Cambridge, Massachusetts.

Baker, J. (1975). The Dragon system – overview. IEEE Transactions on Acoustics, Speech, and Signal processing, 23, 24–29.

Baker, J. (1979). Trainable grammars for speech recognition. In Speech Communication Papers for the 97th Meeting of the Acoustical Society of America, pp. 547–550, Cambridge, Massachusetts. MIT Press.

Baldwin, J. M. (1896). A new factor in evolution. American Naturalist, 30, 441–451. Continued on pages 536–553.

Ballard, B. W. (1983). The *-minimax search procedure for trees containing chance nodes. Artificial Intelligence, 21(3), 327–350.

Baluja, S. (1997). Genetic algorithms and explicit search statistics. In Mozer, M. C. Jordan, M. I., and Petsche, T. (Eds.), Advances in Neural Information Processing Systems, Vol. 9. pp. 319–325. MIT Press, Cambridge, Massachusetts.

Bancilhon, F. Maier, D., Sagiv, Y., and Ullman, J. D. (1986). Magic sets and other strange ways to implement logic programs. In Proceedings of the Fifth ACM Symposium on Principles of Database Systems, pp. 1–16. New York. ACM Press.

Bar-Hillel, Y. (1954). Indexical expressions. Mind, 63, 359–379.

Bar-Hillel, Y. (1960). The present status of automatic translation of languages. In Alt, F. L. (Ed.), Advances in Computers, Vol. 1, pp. 91–163. Academic Press, New York.

Bar-Shalom, Y. (Ed.). (1972). Multitarget-multisensor tracking: Advanced applications. Artech House, Norwood, Massachusetts.

Bar-Shalom, Y. and Fortmann, T. E. (1988). Tracking and Data Association. Academic Press, New York.

Barrett, A. and Weld, D. S. (1994). Task-decomposition via plan parsing. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 1117–1122, Seattle. AAAI Press.

Bartak, R. (2001). Theory and practice of constraint propagation. In Proceedings of the Third Workshop on Constraint Programming for Decision and Control (CPDC-01), pp. 7–14, Gliwice, Poland.

Barto, A. G. Bradtke, S. J., and Singh, S. P. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence, 73(1), 81–138.

Barto, A. G., Sutton, R. S., and Anderson, C. W. (1983). Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man and Cybernetics, 13, 834–846.

Barto, A. G., Sutton, R. S., and Brouwer, P. S. (1981). Associative search network: A reinforcement learning associative memory. Biological Cybernetics, 40(3), 201–211.

Barton, G. E. Berwick, R. C., and Ristad, E. S. (1987). Computational Complexity and Natural Language. MIT Press, Cambridge, Massachusetts.

Barwise, J. and Etchemendy, J. (1993). The Language of First-Order Logic: Including the Macintosh Program Tarski’s World 4.0 (Third Revised and Expanded edition). Center for the Study of Language and Information (CSLI), Stanford, California.

Bateman, J. A. (1997). Enabling technology for multilingual natural language generation: The KPML development environment. Natural Language Engineering, 3(1), 15–55.

Bateman, J. A., Kasper, R. T., Moore, J. D., and Whitney, R. A. (1989). A general organization of knowledge for natural language proecessing: The penman upper model. Tech. rep., Information Sciences Institute, Marina del Rey, CA.

Baum, E., Boneh, D., and Garrett, C. (1995). On genetic algorithms. In Proceedings of the Eighth Annual Conference on Computational Learning Theory (COLT-92), pp. 230–239, Santa Cruz, California. ACM Press.

Baum, E. and Haussler, D. (1989). What size net gives valid generalization?. Neural Computation, 1(1), 151–160.

Baum, E. and Smith, W. D. (1997). A Bayesian approach to relevance in game playing. Artificial Intelligence, 97(1–2), 195–242.

Baum, E. and Wilczek, F. (1988). Supervised learning of probability distributions by neural networks. In Anderson, D. Z. (Ed.), Neural Information Processing Systems, pp. 52–61. American Institute of Physics, New York.

Baum, L. E. and Petrie, T. (1966). Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics, 41.

Baxter, J. and Bartlett, P. (2000). Reinforcement learning in POMDP’s via direct gradient ascent. In Proceedings of the Seventeenth International Conference on Machine Learning, pp. 41–48, Standford, California. Morgan Kaufmann.

Bayardo, R. J. and Schrag, R. C. (1997). Using CSP look-back techniques to solve real-world SAT instances. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97). pp. 203–208. Providence, Rhode Island. AAAI Press.

Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.

Beal, D. F. (1980). An analysis of minimax. In Clarke, M. R. B. (Ed.). Advances in Computer Chess 2, pp. 103–109. Edinburgh University Press, Edinburgh, Scotland.

Beal, D. F. (1990). A generalised quiescence search algorithm. Artificial Intelligence, 43(1), 85–98.

Beckert, B. and Posegga, J. (1995). Leantap: Lean, tableau-based deduction. Journal of Automated Reasoning, 15(3), 339–358.

Beeri, C. Fagin, R., Maier, D., and Yannakakis, M. (1983). On the desirability of acyclic database schemes. Journal of the Association for Computing Machinery, 30(3), 479–513.

Bell, C. and Tate, A. (1985). Using temporal constraints to restrict search in a planner. In Proceedings of the Third Alvey IKBS SIG Workshop, Sunningdale, Oxfordshire, UK. Institution of Electrical Engineers.

Bell, J. L. and Machover, M. (1977). A Course in Mathematical Logic. Elservier/North-Holland, Amsterdam, London, New York.

Bellman, R. E. (1978). An Introduction to Artificial Intelligence: Can Computers Think? Boyd & Fraser Publishing Company, San Francisco.

Bellman, R. E. and Dreyfus, S. E. (1962). Applied Dynamic Programming. Princeton University Press, Princeton, New Jersey.

Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, New Jersey.

Belongie, S., Malik, J., and Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 24(4), 509–522.

Bender, E. A. (1996). Mathematical methods in artificial intelligence. IEEE Computer Society Press, Los Alamitos, California.

Bentham, J. (1823). Principles of Morals and Legislation. Oxford University Press, Oxford, UK. Original work published in 1789.

Berger, J. O. (1985). Statistical Decision Theory and bayesian Analysis. Springer Verlag, Berlin.

Berlekamp, E. R. Conway, J. H., and Guy, R. K. (1982). Winning Ways, For Your Mathematical Plays. Academic Press, New York.

Berleur, J. and Brunnstein, K. (2001). Ethics of Computing: Codes, Spaces for Discussion and Law. Chapman and Hall, London.

Berliner, H. J. (1977), BKG – a program that plays backgammon. Tech. rep., Computer Science Department, Carnegie-Mellon University, Pittsburgh.

Berliner, H. J. (1979). The B* tree search algorithm: A best-first proof procedure. Artificial Intelligence, 12(1), 23–40.

Berliner, H. J. (1980a). Backgammon computer program beats world champion. Artificial Intelligence, 14, 205–220.

Berliner, H. J. (1980b). Computer backgammon. Scientific American, 249(6), 64–72.

Berliner, H. J. and Ebeling, C. (1989). Pattern knowledge and search: The SUPREM architecture. Artificial Intelligence, 38(2), 161–198.

Bernardo, J. M. and Smith, A. F. M. (1994). Bayesian Theory. Wiley, New York.

Berners-Lee, T. Hendler, J., and Lassila, O. (2001). The semantic web. Scientific American, 284(5), 34–43.

Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis. Proceedings of the St. Petersburg Imperial Academy of Sciences, 5, 175–192.

Bernstein, A. and Roberts, M. (1985). Computer vs. chess player. Scientific American, 198(6), 96–105.

Bernstein, A., Roberts, M., Arbuckle, T., and Belsky, M. S. (1958). A chess playing program for the IBM 704. In Proceedings of the 1958 Western Joint Computer Conference, pp. 157–159, Los Angeles. American Institute of Electrical Engineers.

Bernstein, P. L. (1996). Against the Odds: The Remarkable Story of Risk. Wiley, New York.

Berrou, C., Glavieux, A., and Thitimajshima, P. (1993). Near Shannon limit error control-correcting coding and decoding: Turbo-codes. I. In Proc. IEEE International Conference on Communications, pp. 1064–1070, Geneva, Switzerland. IEEE.

Berry, D. A. and Fristedt, B. (1985). Bandit Problems: Sequential Allocation of Experiments. Chapman and Hall, London.

Bertele, U. and Brioschi, F. (1972). Nonserial dynamic programming. Academic Press, New York.

Bertoli, P. Cimatti, A., and Roveri, M. (2001a). Heuristic search + symbolic modell checking = efficient conformant planning. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 467–472. Seattle, Morgan Kaufmann.

Bertoli, P. Cimatti, A., Roveri, M., and Traverso, P. (2001b). Planning in nondeterministic domains under partial observability via symbolic model checking. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 473–478. Seattle. Morgan Kaufmann.

Bertsekas, D. (1987). Dynamic Programming: Deterministic and Stochastic Models. Prentice-Hall, Upper Saddle River, New Jersey.

Bertsekas, D. and Tsitsiklis, J. N. (1996). Neurodynamic programming. Athena Scientific, Belmont, Massachusetts.

Bertsekas, D. and Tsitsiklis, J. N. (2002). Introduction to Probability. Athena Scientific, Belmont, Massachusetts.

Bibel, W. (1981). On matrices with connections. Journal of the Association for Computing Machinery, 28(4), 633–645.

Bibel, W. (1993). Deduction: Automated Logic. Academic Press, London.

Biggs, N. L., Lloyd, E. K., and Wilson, R. J. (1986). Graph Theory 1736–1936. Oxford University Press, Oxford, UK.

Binder, J., Koller, D., Russelll, S. J., and Kanazawa, K. (1997a). Adaptive probabilistic networks with hidden variables. Machine Learning, 29, 213–244.

Binder, J. Murphy, K., and Russel, S. J. (1997b). Space-efficient inference in dynamic probabilistic networks. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1292–1296, Nagoya, Japan. Morgan Kaufmann.

Binford, T. O. (1971). Visual perception by computer. Invited paper presented at the IEEE Systems Science and Cybernetics Conference, Miami.

Binmore, K. (1982). Essays on Foundations of Game Theory. Pitman, London.

Birnbaum, L. and Selfridge, M. (1981). Conceptual analysis of natural language. In Schank, R. and Riesbeck, C. (Eds.), Inside Computer Understanding. Lawrence Erlbaum, Potomac, Maryland.

Biro, J. I. and Shahan, R. W. (Eds.) (1982). Mind, Brain and Function: Essays in the Philosophy of Mind. University of Oklahoma Press, Norman, Oklahoma.

Birtwistle, G. Dahl, O.-J., Myrhaug, B. and Nygaard, K. (1973). Simula Begin. Studentliteratur (Lund) and Auerbach, New York.

Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press, Oxford, UK.

Bistarelli, S. Montanari, U., and Rossi, F. (1997). Semiring-based constraint satisfaction and optimization. Journal of the Association for Computing Machinery, 44(2), 201–236.

Bitner, J. R. and Reingold, E. M. (1975). Backtrack programming techniques. Communications of the Association for Computing Machinery, 18(11), 651–656.

Blei, D. M., Ng, A. Y., and Jordan, M. I. (2001). Latent Dirichlet Allocation. In Neural Information Processing Systems, Vol. 14, Cambridge, Massachusetts, MIT Press.

Blinder, A. S. (1983). Issues in the coordination of monetary and fiscal policies. In Monetary Policy Issues in the 1980s. Federal Reserve Bank, Kansas City, Missouri.

Block, N. (ed.) (1980). Readings in Philosophy of Psychology, Vol. 1. Harvard University Press, Cambridge, Massachusetts.

Blum, A. L. and Furst, M. (1995). Fast planning through planning graph analysis. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), pp. 1636–1642, Montreal Morgan Kaufmann.

Blum, A. L. and Furst, M. (1997). Fast planning through planning graph analysis. Artificial Intelligence, 90(1–2), 281–300.

Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. (1989). Learnability and the Vapnik-Chervonenkis dimension. Journal of the Association for Computing Machinery, 36(4), 929–965.

Bobrow, D. G. (1967). Natural language input for a computer problem solving system. In Minsky, M. L. (Ed.), Semantic Information Processing, pp. 133–215. MIT Press, Cambridge, Massachusetts.

Bobrow, D. G., Kaplan, R., Kay, M., Norman, D. A., Thompson, H., and Winograd, T. (1977). GUS, a frame driven dialog system. Artificial Intelligence, 8, 155–173.

Bobrow, D. G. and Raphael, B. (1974). New programming languages for artificial intelligence research. Computing Surveys, 6(3), 153–174.

Boden, M. A. (1977). Artificial Intelligence and Natural Man. Basic Books, New York.

Boden, M. A. (Ed.) (1990). The Philosophy of Artificial Intelligence, Oxford University Press, Oxford, UK.

Bonet, B. and Geffner, H. (1999). Planning as heuristic search: New results. In Proceedings of the European Conference on Planning, pp. 360–372, Durham, UK. Springer-Verlag.

Bonet, B. and Geffner, H. (2000). Planning with incomplete information as heuristic search in belief space. In Chien, S., Kambhampati, S., and Knoblock, C. A. (Eds.), International Conference on Artificial Intelligence Planning and Scheduling, pp. 52–61, Menlo Park, California. AAAI Press.

Boole, G. (1847). The Mathematical Analysis of Logic: Being an Essay towards a Calculus of Deductiva Reasoning. Macmillan, Barclay, and Macmillan, Cambridge.

Boolos, G. S. and Jeffrey, R. C. (1989). Computability and Logic (3rd edition). Cambridge University Press, Cambridge, UK.

Booth, T. L. (1969). Probabilistic representation of formal languages. In IEEE Conference Record of the 1969 Tenth Annual Symposium on Switching and Automata Theory, pp. 74–81, Waterloo, Ontario. IEEE.

Borel, E. (1921). La théorie du jeu et les équations intégrales à noyau symétrique. Comptes Rendus Hebdomadaires des Séances de l’Académie des Sciences, 173, 1304–1308.

Borenstein, J., Everett, B., and Feng, L. (1996). Navigating Mobile Robots: Systems and Techniques. A. K. Peters, Ltd., Wellesley, MA.

Borenstein, J. and Koren., Y. (1991). The vector field histogram–fast obstacle avoidance for moile robots. IEEE Transactions on Robotics and Automation, 7(3), 278–288.

Borgida, A., Brachman, R. J., McGuinness, D. L., and Alperin Resnick, L. (1989). CLASSIC: A structural data model for objects. SIGMOD Record, 18(2), 58–67.

Boser, B. E. Guyon, I. M. and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (COLT-92), Pittsburgh, Pennsylvania. ACM Press.

Boutilier, C. and Brafman, R. I. (2001). Partial-order planning with concurrent interacting actions. Journal of Artificial Intelligence Research, 14, 105–136.

Boutilier, C., Dearden, R., and Goldszmidt, M. (2000). Stochastic dynamic programming with factored representations. Artificial Intelligence, 121, 49–107.

Boutilier, C. Reiter, R., and Price, B. (2001). Symbolic dynamic programming for first-order MDPs. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 467–472, Seattle, Morgan Kaufmann.

Boutilier, C., Reiter, R., Soutchanski, M., and Thrun, S. (2000). Decision-theoretic, high-level agent programming in the situation calculus. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pp. 355–362, Austin, Texas. AAAI Press.

Box, G. E. P. (1957). Evolutionary operation: A method of increasing industrial productivity. Applied Statistics, 6, 81–101.

Boyan, J. A. (2002). Technical update: Least-squares temporal difference learning. Machine Learning, 49(2–3), 233–246.

Boyan, J. A. and Moore, A. W. (1998). Learning evaluation functions for global optimization and Boolean satisfiability. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), Madison, Wisconsin. AAAI Press.

Boyen, X., Friedman, N., and Koller, D. (1999). Discovering the hidden structure of complex dynamic systems. In Uncertainty in Artificial Intelligence: Proceedings of the Fifteenth Conference, Stockholm. Morgan Kaufmann.

Boyer, R. S. and Moore, J. S. (1979). A Computational Logic. Academic Press, New York.

Boyer, R. S. and Moore, J. S. (1984). Proof checking the RSA public key encryption algorithm. American Mathematical Monthly, 91(3), 181–189.

Brachman, R. J. (1979). On the epistemological status of semantic networks. In Findler, N. V. (Ed.), Associative Networks: Representation and Use of Knowledge by Computers, pp. 3–50. Academic Press, New York.

Brachman, R. J., Fikes, R. E., and Levesque, H. J. (1983). Krypton: A functional approach to knowledge representation. Computer, 16(10), 67–73.

Brachman, R. J. and Levesque, H. J. (Eds.). (1985). Readings in Knowledge Representation. Morgan Kaufmann, San Mateo, California.

Bradtke, S. J. and Barto, A. G. (1996). Linear least-squares algorithms for temporal difference learning. Machine Learning, 22, 33–57.

Brafman, R. I. and Tennenholtz, M. (2000). A near optimal polynomial time algorithm for learning in certain classes of stochastic games. Artificial Intelligence, 121, 31–47.

Braitenberg, V. (1984). Vehicles: Experiments in Synthetic Psychology. MIT Press.

Bransford, J. and Johnson, M. (1973). Consideration of some problems in comprehension. In Chase, W. G. (Ed.), Visual Information Processing. Academic Press, New York.

Bratko, I. (1986). Prolog Programming for Artificial Intelligence (1st edition). Addison–Wesley, Reading, Massachusetts.

Bratko, I. (2001). Prolog Programming for Artificial Intelligence (Third edition). Addison-Wesley, Reading, Massachusetts.

Bratman, M. E. (1987). Intention, Plans, and Practical Reason. Harvard University Press, Cambridge, Massachusetts.

Bratman, M. E. (1992). Planning and the stability of intention. Minds and Machines, 2(1), 1–16.

Breese, J. S. and Heckerman, D. (1996). Decision-theoretic troubleshooting: A framework for repair and experiment. In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference, pp. 124–132, Portland, Oregon, Morgan Kaufmann.

Breiman, L. (1996). Bagging predictors. Machine Learning, 26(2), 123–140.

Breiman, L., Friedman, J., Olshen, R. A., and Stone, P. J. (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.

Brelaz, D. (1979). New methods to color the vertices of a graph. Communications of the Association for Computing Machinery, 22(4), 251–256.

Brent, R. P. (1973). Algorithms for minimization without derivatives. Prentice-Hall, Upper Saddle River, New Jersey.

Bresnan, J. (1982). The Mental Representation of Grammatical Relations. MIT Press, Cambridge, Massachusetts.

Brewka, G., Dix, J., and Konolige, K. (1997). Nononotonic Reasoning: An Overview. CSLI Publications, Stanford, California.

Bridle, J. S. (1990). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In Fogelman Soulié, F. and Hérault, J. (Eds.), Neurocomputing: Algorithms, Architectures and Applications. Springer-Verlag, Berlin.

Briggs, R. (1985). Knowledge representation in Sanskrit and artificial intelligence. Al Magazine, 6(1), 32–39.

Brin, S. and Page, L. (1998). The anatomy of a largescale hypertextual web search engine. In Proceedings of the Seventh World Wide Web Conference, Brisbane, Australia.

Broadbent, D. E. (1958). Perception and communication. Pergamon, Oxford, UK.

Brooks, R. A. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2, 14–23.

Brooks, R. A. (1989). Engineering approach to building complete, intelligent beings. Proceedings of the SPIE – the International Society for Optical Engineering, 1002, 618–625.

Brooks, R. A. (1990). Elephants don’t play chess. Autonomous Robots, 6, 3–15.

Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1–3), 139–159.

Brooks, R. A. and Lozano-Perez, T. (1985). A subdivision algorithm in configuration space for findpath with rotation. IEEE Transactions on Systems, Man and Cybernetics, 15(2), 224–233.

Brown, M. Grundy, W., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, M., and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data using support vector machines. In Proceedings of the national Academy of Sciences, Vol. 97, pp. 262–267.

Brown, P. F., Cocke, J., Della Pietra, S. A., Della Pietra, V. J., Jelinek, F., Mercer, R. L., and Rossin, P. (1988). A statistical approach to language translation. In Proceedings of the 12th International Conference on Computational Linguistics, pp. 71–76, Budapest, John von Neumann Society for Computing Sciences.

Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., and Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–311.

Brownston, L., Farrell, R., Kant, E., and Martin, N. (1985). Programming expert systems in OPS5: An introduction to rule-based programming. Addison-Wesley, Reading, Massachusetts.

Brudno, A. L. (1963). Bounds and valuations for shortening the scanning of variations. Problems of Cybernetics, 10, 225–241.

Bruner, J. S., Goodnow, J. J., and Austin, G. A. (1957). A Study of Thinking. Wiley, New York.

Bryant, R. E. (1992). Symbolic Boolean manipulation with ordered binary decision diagrams. ACM Computing Surveys, 24(3), 293–318.

Bryson, A. E. and Ho, Y.-C. (1969). Applied Optimal Control. Blaisdell, New York.

Buchanan, B. G. and Mitchell, T. M. (1978). Model-directed learning of production rules. In Waterman, D. A. and Hays-Roth, F. (Eds.), Pattern-Directed Inference Systems, pp. 297–312. Academic Press, New York.

Buchanan, B. G., Mitchell, T. M., Smith, R. G., and Johnson, C. R. (1978). Models of learning systems. In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker, New York.

Buchanan, B. G., and Shortliffe, E. H. (Eds.). (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading, Massachusetts.

Buchanan, B. G., Sutherland, G. L., and Feigenbaum, E. A. (1969). Heuristic DENDRAL: A program for generating explanatory hypotheses in organic chemistry. In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 209–254. Edinburgh University Press, Edinburgh, Scotland.

Bundy, A. (1999). A survey of automated deduction. In Wooldridge, M. J. and Veloso, M. (Eds.), Artificial intelligence today: Recent trends and developments, pp. 153–174. Springer-Verlag, Berlin.

Bunt, H. C. (1985). The formal representation of (quasi-) continuous concepts. In Hobbs, J. R. and Moore, R. C. (Eds.), Formal Theories of the Commonsense World, chap. 2, pp. 37–70. Ablex, Norwood, New Jersey.

Burgard, W., Cremers, A. B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., and Thrun, S. (1999). Experiences with an interactive museum tourguide robot. Artificial Intelligence, 114(1–2), 3–55.

Buro, M. (2002). Improving heuristic mini-max search by supervised learning. Artificial Intelligence, 134(1–2), 85–99.

Burstall, R. M. (1974). Program proving as hand simulation with a little induction. In Information Processing ’74, pp. 308–312. Elsevier/North-Holland, Amsterdam, London, New York.

Burstall, R. M. and Darlington, J. (1977). A transformation system for developing recursive programs. Journal of the Association for Computing Machinery, 24(1), 44–67.

Burstein, J., Leacock, C., and Swartz, R. (2001). Automated evaluation of essays and short answers. In Fifth International Computer Assisted Assessment (CAA) Conference, Loughborough, U.K. Loughborough University.

Bylander, T. (1992). Complexity results for serial decomposability. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 729–734, San Jose. AAAI Press.

Bylander, T. (1994). The computational complexity of propositional strips planning. Artificial Intelligence, 69, 165–204.

Calvanese, D., Lenzerini, M., and Nardi, D. (1999). Unifying class-based representation formalism. Journal of Artificial Intelligence Research, 11, 199–240.

Campbell, M. S., Hoane, A. J., and Hsu, F.-H. (2002). Deep Blue. Artificial Intelligence, 134(1–2), 57–83.

Canny, J. and Reif, J. (1987). New lower bound techniques for robot motion planning problems. In IEEE Symposium on Foundations of Computer Science, pp. 39–48.

Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 8, 679–698.

Canny, J. (1988). The Complexity of Robot Motion Planning. MIT Press. Cambridge, Massachusetts.

Carbonell, J. G. (1983). Derivational analogy and its role in problem solving. In Proceedings of the National Conference on Artificial Intelligence (AAAI-83), pp. 64–69, Washington, DC. Morgan Kaufmann.

Carbonell, J. G. Knoblock, C. A., and Minton, S. (1989). PRODIGY: An integrated architecture for planning and learning. Technical report CMU-CS-89-189, Computer Science Department, Carnegie-Mellon University, Pittsburgh.

Carbonell, J. R. and Collins, A. M. (1973). Natural semantics in artificial intelligence. In Proceedings of the Third International Joint Conference on Artificial Intelligence (IJCAI-73), pp. 344–351, Stanford, California. IJCAII.

Carnap, R. (1928). Der logische Aufbau der Welt. Weltkreis-verlag, Berlin-Schlachtensee. Translated into English as (Carnap, 1967).

Carnap, R. (1948). On the application of inductive logic. Philosophy and Phenomenological Research, 8, 133–148.

Carnap, R. (1950). Logical Foundations of Probability. University of Chicago Press, Chicago.

Carrasco, R. C., Oncina, J., and Calera, J. (1998). Stochastic Inference of Regular Tree Languages, Vol. 1433 of Lecture Notes in Computer Science. Springer-Verlag, Berlin.

Cassandra, A. R. Kaelbling, L., P., and Littman, M. L. (1994). Acting optimally in partially observable stochastic domains. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 1023–1028. Seattle. AAAI Press.

Ceri, S., Gottlob, G., and Tanca, L. (1990). Logic programming and databases. Springer-Verlag, Berlin.

Chakrabarti, P. P., Ghose, S., Acharya, A., and de Sarkar, S. C. (1989). Heuristic search in restricted memory. Artificial Intelligence, 41(2), 197–222.

Chan, W. P., Prete, F., and Dickinson, M. H. (1998). Visual input to the efferent control system of a fly’s ’gyroscope’. Science, 289, 289–292.

Chandra, A. K. and Harel, D. (1980). Computable queries for relational data bases. Journal of Computer and System Sciences, 21(2), 156–178.

Chandra, A. K. and Merlin, P. M. (1977). Optimal implementation of conjunctive queries in relational databases. In Proceedings of the 9th Annual ACM Symposium on Theory of Computing, pp. 77–90, New York. ACM Press.

Chang, C.-L. and Lee, R. C.-T. (1973). Symbolic Logic and Mechanical Theorem Proving. Academic Press, New York.

Chapman, D. (1987). Planning for conjunctive goals. Artificial Intelligence, 32(3), 333–377.

Charniak, E. (1993). Statistical Language Learning. MIT Press, Cambridge, Massachusetts.

Charniak, E. (1996). Tree-bank grammars. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 1031–1036, Portland, Oregon. AAAI Press.

Charniak, E. (1997). Statistical parsing with a context-free grammar and word statistics. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), pp. 598–603, Providence, Rhode Island. AAAI Press.

Charniak, E. and Goldman, R. (1992). A Bayesian model of plan recognition. Artificial Intelligence, 64(1) 53–79.

Charniak, E. and McDermott, D. (1985). Introduction to Artificial Intelligence. Addison-Wesley, Reading, Massachusetts.

Charniak, E., Riesbeck, C., McDermott, D., and Meehan, J. (1987). Artificial Intelligence Programming (2nd edition). Lawrence Erlbaum Associates, Potomac, Maryland.

Chatfield, C. (1989). The Analysis of Time Series: An Introduction (4th edition). Chapman and Hall, London.

Cheeseman, P. (1985). In defense of probability. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85), pp. 1002–1009, Los Angeles, Morgan Kaufmann.

Cheeseman, P. (1988). An inquiry into computer understanding. Computational Intelligence, 4(1), 58–66.

Cheeseman, P., Kanefsky, B., and Taylor, W. (1991). Where the really hard problems are. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91), pp. 331–337, Sydney. Morgan Kaufmann.

Cheeseman, P., Self, M., Kelly, J., and Stutz, J. (1988). Bayesian classification. In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), Vol. 2, pp. 607–611, St. Paul, Minnesota, Morgan Kaufmann.

Cheeseman, P. and Stutz, J. (1996). Bayesian classification (AutoClass): Theory and results. In Fayyad, U., Piatesky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining. AAAI Press/MIT Press, Menlo Park, California.

Cheng, J. and Druzdzel, M. J. (2000). AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Journal of Artificial Intelligence Research, 13, 155–188.

Cheng, J. Greiner, R., Kelly, J., Bell, D. A., and Liu, W. (2002). Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence, 137, 43–90.

Chierchia, G. and McConnell-Ginet, S. (1990). Meaning and Grammar. MIT Press, Cambridge, Massachusetts.

Chomsky, N. (1956). Three models for the description of language. IRE Transactions on Information Theory, 2(3), 113–124.

Chomsky, N. (1957). Syntactic Structures. Mouton, The Hague and Paris.

Chomsky, N. (1980). Rules and representations. The Behavioral and Brain Sciences, 3, 1–61.

Choset, H. (1996). Sensor Based Motion Planning: The Hierarchical Generalized Voronoi Graph. Ph.D. thesis, California Institute of Technology.

Chung, K. L. (1979). Elementary Probability Theory with Stochastic Processes (3rd edition). Springer-Verlag, Berlin.

Church, A. (1936). A note on the Entscheidungsproblem. Journal of Symbolic Logic, 1, 40–41 and 101–102.

Church, K. and Patil, R. (1982). Coping with syntactic ambiguity or how to put the block in the box on the table. American Journal of Computational Linguistics, 8(3–4), 139–149.

Church, K. and Gale, W. A. (1991). A comparison of the enhanced Good-Turing and deleted estimation methods for estimating probabilities of English bigrams. Computer Speech and Language, 5, 19–54.

Churchland, P. M. (1979). Scientific Realism and the Plasticity of Mind. Cambridge University Press, Cambridge, UK.

Churchland, P. M. and Churchland, P. S. (1982). Functionalism, qualia, and intentionality. In Biro, J. I. and Shahan, R. W. (Eds.), Mind, Brain and Function: Essays in the Philosophy of Mind, pp. 121–145. University of Oklahoma Press, Norman, Oklahoma.

Churchland, P. S. (1986). Neurophilosophy: Toward a Unifed Science of the Mind–Brain. MIT Press, Cambridge, Massachusetts.

Cimatti, A., Roveri, M., and Traverso, P. (1998). Automatic OBDD-based generation of universal plans in non-determistic domains. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 875–881, Madison, Wisconsin. AAAI Press.

Clark, K. L. (1978). Negation as failure. In Gallaire, H. and Minker, J. (Eds.), Logic and Data Bases, pp. 293–322. Plenum, New York.a

Clark, P. and Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3, 261–283.

Clarke, A. C. (1968a). 2001: A Space Odyssey. Signet.

Clarke, A. C. (1968b). The world of 2001. Vogue.

Clarke, E. and Grumberg, O. (1987). Research on automtic verification of finite-state concurrent systems. Annual Review of Computer Science, 2, 269–290.

Clarke, E. Grumberg, O., and Peled, D. (1999). Model Checking. MIT Press, Cambridge, Massachusetts.

Clarke, M. R. B. (Ed.). (1977). Advances in Computer Chess 1. Edinburgh University Press, Scotland.

Clearwater, S. H. (Ed.). (1996). Market-Based Control. World Scientific, Singapore and Teneck, New Jersey.

Clocksin, W. F. and Mellish, C. S. (1994). Programming in Prolog (4th edition). Springer-Verlag, Berlin.

Clowes, M. B. (1971). On seeing things. Artificial Intelligence, 2(1), 79–116.

Cobham, A. (1964). The intrinsic computational difficulty of functions. In Bar-Hillel, Y. (Ed.), Proceedings of the 1964 International Congress for Logic, Methodology, and Philosophy of Science, pp. 24–30, Jerusalem. Elsevier/North-Holland.

Cobley, P. (1997). Introducting Semiotics. Totem Books, New York.

Cohen, J. (1988). A view of the origins and development of PROLOG. Communications of the Association for Computing Machinery, 31, 26–36.

Cohen, P. R. (1995). Empirical methods for artificial intelligence. MIT Press, Cambridge, Massachusetts.

Cohen, P. R. and Levesque, H. J. (1990). Intention is choice with commitment. Artificial Intelligence, 42(2–3), 213–261.

Cohen, P. R., Morgan, J., and Pollack, M. E. (1990). Intentions in Communication. MIT Press, Cambridge, Massachusetts.

Cohen, P. R. and Perrault, C. R. (1979). Elements of a plan-based theory of speech acts. Cognitive Science, 3(3), 177–212.

Cohen, W. W. and Page, C. D. (1995). Learnability in inductive logic programming: Methods and results. New Generation Computing, 13(3–4), 369–409.

Cohn, A. G. Bennett, B., Gooday, J. M., and Gotts, N. (1997). RCC: A calculus for region based qualitative spatial reasoning. GeoInformatica, 1, 275–316.

Collins, M. J. (1996). A new statistical parser based on bigram lexical dependencies. In Joshi, A. K. and Palmer, M. (Eds.), Proceedings of the Thirty-Fourth Annual Meeting of the Association for Computational Linguistics, pp. 184–191, San Francisco, Morgan Kaufmann Publishers.

Collins, M. J. (1999). Head-driven Statistical Models for Natural Language Processing. Ph.D. thesis, University of Pennsylvania.

Collins, M. and Duffy, K. (2002). New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In Proceedings of the ACL.

Colmerauer, A. (1975). Les grammaires de metamorphose. Tech. rep., Groupe d’Intelligence Artificielle, Université de Marsaille-Luminy.

Colmerauer, A., Kanoui, H., Pasero, R., and Roussel, P. (1973). Un systéme de communication homme– machine en Français. Rapport, Groupe d’Intelligence Artificielle, Université d’Aix-Marseille II.

Condon, J. H. and Thompson, K. (1982). Belle chess harware. In Clarke, M. R. B. (Ed.), Advances in Computer Chess 3, pp. 45–54. Pergamon, Oxford, UK.

Congdon, C. B., Huber, M., Kortenkamp, D., Bidlack, C., Cohen, C., Huffman, S., Koss, F., Raschke, U., and Weymouth, T. (1992). CARMEL versus Flakey: A comparison of two robots. Tech. rep. Papers from the AAAI Robot Competition, RC-92-01, American Association for Artificial Intelligence, Menlo Park, CA.

Connell, J. (1989). A Colony Architecture for an Artificial Creature. Ph.D. thesis, Artificial Intelligence Laboratory, MIT, Cambridge, MA. also available as AI Technical Report 1151.

Cook, S. A. (1971). The complexity of theorem-proving procedures. In Proceedings of the 3rd Annual ACM Symposium on Theory of Computing, pp. 151–158, New York. ACM Press.

Cook, S. A. and Mitchell, D. (1997). Finding hard instances of the satisfiability problem: A survey. In Du, D., Gu, J., and Pardalos, P. (Eds.), Satisfiability problems: Theory and applications. American Mathematical Society, Providence, Rhode Island.

Cooper, G. (1990). The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 42, 393–405.

Cooper, G. and Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309–347.

Copeland, J. (1993). Artificial Intelligence: A Philosophical Introduction. Blackwell, Oxford, UK.

Copernicus (1543). De Revolutionibus Orbium Coelestium. Apud Ioh. Petreium, Nuremberg.

Cormen, t. H., Leiserson, C. E., and Rivest, R. (1990). Introduction to Algorithms. MIT Press, Cambridge, Massachusetts.

Cortes, C. and Vapnik, V. N. (1995). Support vector networks. Machine Learning, 20, 273–297.

Cournot, A. (Ed.) (1838). Recherches sur les principes mathématiques de la théorie des richesses. L. Hachette, Paris.

Covington, M. A., (1994). Natural Language Processing for Prolog Programmers. Prentice-Hall, Upper Saddle River, New Jersey.

Cowan, J. D. and Sharp, D. H. (1988a). Neural nets. Quarterly Reviews of Biophysics, 21, 365–427.

Cowan, J. D. and Sharp, D. H. (1988b). Neural nets and artificial intelligence. Daedalus, 117, 85–121.

Cox, I. (1993). A review of statistical data association techniques for motion correspondence. International Journal of Computer Vision, 10, 53–66.

Cox, I. and Hingorani, S. L. (1994). An efficient implementation and evaluation of Reid’s multiple hypothesis tracking algorithm for visual tracking. In Proceedings of the 12th International Conference on Pattern Recognition, Vol. 1, pp. 437–442, Jerusalem, Israel. International Association for Pattern Recognition (IAPR).

Cox, I. and Wilfong, G. T. (Eds.). (1990). Autonomous Robot Vehicles. Springer-Verlag, Berlin.

Cox, R. T. (1946). Probability, frequency, and reasonable expectation. American Journal of Physics, 14(1), 1–13.

Craig, J. (1989). Introduction to Robotics: Mechanics and Control (2nd Edition). Addison-Wesley Publishing, Inc., Reading, MA.

Craik, K. J. (1943). The Nature of Explanation. Cambridge University Press, Cambridge, UK.

Crawford, J. M. and Auton, L. D. (1993). Experimental results on the crossover point in satisfiability problems. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 21–27, Washington, DC. AAAI Press.

Cristianini, N. and Schölkopf, B. (2002). Support vector machines and kernel methods: The new generation of learning machines. AI Magazine, 23(3), 31–41.

Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, UK.

Crockett, L. (1994). The Turing Test and the Frame Problem: AI’s Mistaken Understanding of Intelligence. Ablex, Norwood, New Jersey.

Cross, S. E. and Walker, E. (1994). Dart: Applying knowledge based planning and scheduling to crisis action planning. In Zweben, M. and Fox, M. S. (Eds.), Intelligent Scheduling, pp. 711–729. Morgan Kaufmann, San Mateo, California.

Cruse, D. A. (1986). Lexical Semantics. Cambridge University Press.

Culberson, J. and Schaeffer, J. (1998). Pattern databases. Computational Intelligence, 14(4), 318–334.

Cullingford, R. E. (1981). Integrating knowledge sources for computer „understanding” tasks. IEEE Transactions on Systems, Man and Cybernetics (SMC), 11.

Cussens, J. and Dzeroski, S. (2000). Learning Language in Logic, Vol. 1925 of Lecture Notes in Computer Science. Springer-Verlag, Berlin.

Cybenko, G. (1988). Continuous valued neural networks with two hidden layers are sufficient. Technical report, Department of Computer Science, Tufts University, Medford, Massachusetts.

Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Controls, Signals, and Systems, 2, 303–314.

Daganzo, C. (1979). Multinomial probit: The theory and its application to demand forecasting. Academic Press, New York.

Dagum, P. and Luby, M. (1993). Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, 60(1), 141–153.

Dahl, O.-J., Myrhaug, B., and Nygaard, K. (1970). (Simula 67) common base language. Tech. rep. N. S-22, Norsk Regnesentral (Norwegian Computing Center), Oslo.

Dale, R., Moisl, H., and Somers, H. (2000). Handbook of Natural Language Processing. Marcel Dekker, New York.

Dantzig, G. B. (1949). Programming of interdependent activities: II. mathematical model. Econometrica, 17, 200–211.

Darwiche, A. (2001). Recursive conditioning. Artificial Intelligence, 126, 5–41.

Darwiche, A. and Ginsberg, M. L. (1992). A symbolic generalization of probability theory. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 622–627, San Jose. AAAI Press.

Darwin, C. (1859). On The Origin of Species by Means of Natural Selection. J. Murray, London.

Darwin, C. (1871). Descent of Man. J. Murray.

Dasgupta, P., Chakrabarti, P. P., and DeSarkar, S. C. (1994). Agent searching in a tree and the optimality of iterative deepening. Artificial Intelligence, 71, 195–208.

Davidson, D. (1980). Essays on Actions and Events. Oxford University Press, Oxford, UK.

Davies, T. R. (1985). Analogy. Informal note INCSLI-85-4, Center for the Study of Language and Information (CSLI), Stanford, California.

Davies, T. R. and Russell, S. J. (1987). A logical approach to reasoning by analogy. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), Vol. 1, pp. 264–270. Milan. Morgan Kaufmann.

Davis, E. (1986). Representing and Acquiring Geographic Knowledge. Pitman and Morgan Kaufmann, London and San Mateo, California.

Davis, E. (1990). Representations of Commonsense Knowledge. Morgan Kaufmann, San Mateo, California.

Davis, K. H., Biddulph, R., and Balashek, S. (1952). Automatic recognition of spoken digits. Journal of the Acoustical Society of America, 24(6), 637–642.

Davis, M. (1957). A computer program for Presburger’s algorithm. In Robinson, A. (Ed.), Proving Theorems (as Done by Man, Logician, or Machine), pp. 215–233, Cornell University, Ithaca, New York. Communications Research Division, Institute for Defense Analysis. Proceedings of the Summer Institute for Symbolic Logic. Second edition; publication date is 1960.

Davis, M., Logemann, G., and Loveland, D. (1962). A machine program for theorem-proving. Communications of the Association for Computing Machinery, 5, 394–397.

Davis, M. and Putnam, H. (1960). A computing procedure for quantification theory. Journal of the Association for Computing Machinery, 7(3), 201– 215.

Davis, R. and Lenat, D. B. (1982). Knowledge-Based Systems in Artificial Intelligence. McGraw-Hill, New York.

Dayan, P. (1992). The convergence of TD(λ) for general λ. Machine Learning, 8(3–4), 341–362.

Dayan, P. and Abbott, L. F. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge, Massachusetts.

de Dombal, F. T., Leaper, D. J., Horrocks, J. C., and Staniland, J. R. (1974). Human and computer-aided diagnosis of abdominal pain: Further report with emphasis on performance of clinicians. British Medical Journal, 1, 376–380.

de Dombal, F. T., Staniland, J. R., and Clamp, S. E. (1981). Geographical variation in disease presentation. Medical Decision Making, 1, 59–69.

de Finetti, B. (1937). Le prévision: ses lois logiques, ses sources subjectives. Ann. Inst. Poincaré, 7, 1–68.

de Freitas, J. F. G., Niranjan, M., and Gee, A. H. (200). Sequential Monte Carlo methods to train neural network models. Neural Computation, 12(4), 933–953.

de Kleer, J. (1975). Qualitative and quantitative knowledge in classical mechanics. Tech. rep. AI-TR-352, MIT Artificial Intelligence Laboratory.

de Kleer, J. (1986a). An assumption-based TMS. Artificial Intelligence, 28(2), 127–162.

de Kleer, J. (1986b). Extending the ATMS. Artificial Intelligence, 28(2), 163–196.

de Kleer, J. (1986c). Problem solving with the ATMS. Artificial Intelligence, 28(2), 197–224.

de Kleer, J. (1989). A comparison of ATMS and CSP techniques. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Vol. 1, pp. 290–296, Detroit. Morgan Kaufmann.

de Kleer, J. and Brown, J. S. (1985). A qualitative physics based on confluences. In Hobbs, J. R. and Moore, R. C. (Eds.), Formal Theories of the Commonsense World, chap. 4, pp. 109–183. Ablex, Norwood, New Jersey.

de Marcken, C. (1996). Unsupervised Language Acquisition. Ph.D. thesis, MIT.

De Morgan, A. (1864). On the syllogism IV and on the logic of relations. Cambridge Philosophical Transactions, x, 331–358.

De Readt, L. (1992). Interactive Theory Revision: An Inductive Logic Programming Approach. Academic Press, New York.

de Saussure, F. [1910 (republished 1993)]. Lectures on General Linguistics. Pergamon Press, Oxford, UK.

Deacon, T. W. (1997). The symbolic species: The coevolution of language and the brain. W. W. Norton, New York.

Deale, M., Yvanovich, M., Schnitzius, D., Kautz, D., Carpenter, M., Zweben, M., Davis, G., and Daun, B. (1994). The space shuttle ground processing scheduling system. In Zweben, M. and Fox, M. (Eds.), Intelligent Scheduling, pp. 423–449. Morgan Kaufmann, San Mateo, California.

Dean, T., Basye, K., Chekaluk, R., and Hyun, S. (1990). Coping with uncertainty in a control system for navigation and exploration. In Preceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Vol. 2, pp. 1010–1015, Boston. MIT Press.

Dean, T. and Boddy, M. (1988). An analysis of time dependent planning. In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), pp. 49á–54, St. Paul, Minnesota. Morgan Kaufmann.

Dean, T., Firby, R. J., and Miller, D. (1990). Hierarchical planning involving deadlines, travel time, and resources. Computational Intelligence, 6(1), 381–398.

Dean, T., Kaelbling, L. P., Kirman, J., and Nicholson, A. (1993). Planning with deadlines in stochastic domains. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 574–579, Washington, DC. AAAI Press.

Dean, T. and Kanazawa, K. (1989a). A model for projection and action. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pp. 985–990, Detroit. Morgan Kaufmann.

Dean, T. and Kanazawa, K. (1989b). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.

Dean, T., Kanazawa, K., and Shewchuk, J. (1990). Prediction, observation and estimation in planning and control. In 5th IEEE International Symposium on Intelligent Control, Vol. 2, pp. 645–650, Los Alamitos, CA. IEEE Computer Society Press.

Dean, T. and Wellman, M. P. (1991). Planning and Control. Morgan Kaufmann, San Mateo, California.

Debevec, P., Taylor, C., and Malik, J. (1996). Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach. In Proceedings. of the 23rd Annual Conference on Computer Graphics (SIGGRAPH), pp. 11–20.

Debreu, G. (1960). Topological methods in cardinal utility theory. In Arrow, K. J., Karlin, S., and Suppes, P. (Eds.), Mathematical Methods in the Social Sciences, 1959. Stanford University Press, Stanford, California.

Dechter, R. (1990a). Enhancement schemes for constraint processing: Backjumping, learning and cutset decomposition. Artificial Intelligence, 41, 273–312.

Dechter, R. (1990b). On the expressiveness of networks with hidden variables. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 379–385, Boston. MIT Press.

Dechter, R. (1992). Constraint networks. In Shapiro, S. (Ed.), Encyclopedia of Artificial Intelligence (2nd edition), pp. 276–285. Wiley and Sons, New York.

Dechter, R. (1999). Bucket elimination: A unifying framework for reasoning. Artificial Intelligence, 113, 41–85.

Dechter, R. and Frost, D. (1999). Backtracking algorithms for constraint satisfaction problems. Tech. rep., Department of Information and Computer Science, University of California, Irvine.

Dechter, R. and Pearl, J. (1985). Generalized best-first search strategies and the optimality of A*. Journal of the Association for Computing Machinery, 32(3), 505–536.

Dechter, R. and Pearl, J. (1987). Network-based heuristics for constraint-satisfaction problems. Artificial Intelligence, 34(1), 1–38.

Dechter, R. and Pearl, J. (1989). Tree clustering for constraint networks. Artificial Intelligence, 38(3), 353–366.

DeCoste, D. and Scholkopf, B. (2002). Training invariant support vector machines. Machine Learning, 46(1), 161–190.

Dedekind, R. (1888). Was sind und was sollen die Zahlen. Braunschweig, Germany.

Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, r. A. (1990). Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6), 391–407.

DeGroot, M. H. (1970). Optimal Statistical Decisions. McGraw-Hill, New York.

DeGroot, M. H. (1989). Probability and Statistics (2nd edition). Addison-Wesley, Reading, Massachusetts.

DeJong, G. (1981). Generalizations based on explanations. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), pp. 67–69, Vancouver, British Columbia. Morgan Kaufmann.

DeJong, G. (1982). An overview of the FRUMP system. In Lehnert, W. and Ringle, M. (Eds.), Strategies for Natural Language Processing, pp. 149–176. Lawrence Erlbaum, Potomac, Maryland.

DeJong, G. and Mooney, R. (1986). Explanation-based learning: An alternative view. Machine Learning, 1, 145–176.

Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, 30 (Series B), 205–247.

Dempster, A. P., Laird, N., and Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39 (Series B), 1–38.

Denes, P. (1959). The design and operation of the mechanical speech recognizer at University College London. Journal of the British Institution of Radio Engineers, 19(4), 219–234.

Deng, X. and Papadimitriou, C. H. (1990). Exploring an unknown graph. In Proceedings 31st Annual Symposium on Foundations of Computer Science, pp. 355–361, St. Louis. IEEE Computer Society Press.

Denis, F. (2001). Learning regular languages from simple positive examples. Machine Learning, 44(1/2), 37–66.

Dennett, D. C. (1971). Intentional systems. The Journal of Philosophy, 68(4), 87–106.

Dennett, D. C. (1978). Why you can’t make a computer that feels pain. Synthese, 38(3).

Dennett, D. C. (1984). Cognitive wheels: the frame problem of AI. In Hookway, C. (Ed.), Minds, Machines, and Evolution: Philosophical Studies, pp. 129–151. Cambridge University Press, Cambridge, UK.

Deo, N. and Pang, C.-Y. (1984). Shortest path algorithms: Taxonomy and annotation. Networks, 14(2), 275–323.

Descartes, R. (1637). Discourse on method. In Cottingham, J., Stoothoff, R., and Murdoch, D. (Eds.), The Philosophical Writings of Descartes, Vol. I. Cambridge University Press, Cambridge, UK.

Descotte, Y. and Latombe, J.-C. (1985). Making compromises among antagonist constraints in a planner. Artificial Intelligence, 27, 183–217.

Devroye, L. (1987). A course in density estimation. Birkhauser, Boston.

Devroye, L., Gyorfi, L., and Lugosi, G. (1996). A probabilistic theory of pattern recognition. Springer-Verlag, Berlin.

Dickmanns, E. D. and Zapp, A. (1987). Autonomous high speed road vehicle guidance by computer vision. In Isermann, R. (Ed.), Automatic Control– World Congress, 1987: Selected Papers from the 10th Triennial World Congress of the International Federation of Automatic Control, pp. 221–226, Munich. Pergamon.

Dietterich, T. (1990). Machine Learning. Annual Review of Computer Science, 4, 255–306.

Dietterich, T. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13, 227–303.

DiGioia, A. M., Kanade, T., and Wells, P. (1996). Final report of the second international workshop on robotics and computer assisted medical interventions. Computer Aided Surgery, 2, 69–101.

Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.

Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H., and Csorba, M. (2001). A solution to the simultaneous localisation and map building (SLAM) problem. IEEE Transactions of Robotics and Automation, 17(3), 229–241.

Do, M. B. and Kambhampati, S. (2001). Sapa: A domain-independent heuristic metric temporal planner. In Proceedings of the European Conference on Planning, Toledo, Spain. Springer-Verlag.

Domingos, P. and Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero–one loss. Machine Learning, 29, 103–130.

Doran, C., Egedi, D., Hockey, B. A., Srinivas, B., and Zaidel, M. (1994). XTAG system–a wide coverage grammar of English. In Nagao, M. (Ed.), Proceedings of the 15th COLING, Kyoto, Japan.

Doran, J. and Michie, D. (1966). Experiments with the graph traverser program. Proceedings of the Royal Society of London, 294, Series A, 235–259.

Dorf, R. C. and Bishop, R. H. (1999). Modern Control Systems. Addison-Wesley, Reading, Massachusetts.

Doucet, A. (1997). Monte Carlo methods for Bayesian estimation of hidden Markov models: Application to radiation signals. Ph.D. thesis, Université de Paris-Sud, Orsay, France.

Dowling, W. F. and Gallier, J. H. (1984). Linear-time algorithms for testing the satisfiability of propositional Horn formulas. Journal of Logic Programming, 1, 267–284.

Dowty, D., Wall, R., and Peters, S. (1991). Introduction to Montague Semantics. D. Reidel, Dordrecht, Netherlands.

Doyle, J. (1979). A truth maintenance system. Artificial Intelligence, 12(3), 231–272.

Doyle, J. (1983). What is rational psychology? Toward a modern mental philosophy. AI Magazine, 4(3), 50–53.

Doyle, J. and Patil, R. (1991). Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services. Artificial Intelligence, 48(3), 261–297.

Drabble, B. (1990). Mission scheduling for spacecraft: Diaries of t-sched. In Expert Planning Systems, pp. 76–81, Brighton, UK. Institute of Electrical Engineers.

Draper, D., Hanks, S., and Weld, D. S. (1994). Probabilistic planning with information gathering and contingent execution. In Proceedings of the Second International Conference on AI Planning Systems, pp. 31–36, Chicago. Morgan Kaufmann.

Dreyfus, H. L. (1972). What Computers Can’t Do: A Critique of Artificial Reason. Harper and Row, New York.

Dreyfus, H. L. (1992). What Computers Still Can’t Do: A Critique of Artificial Reason. MIT Press, Cambridge, Massachusetts.

Dreyfus, H. L. and Dreyfus, S. E. (1986). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Blackwell, Oxford, UK.

Dreyfus, S. E. (1969). An appraisal of some shortest-paths algorithms. Operations Research, 17, 395–412.

Du, D., Gu, J., and Pardalos, P. M. (Eds.). (1999). Optimization methods for logical inference. American Mathematical Society, Providence, Rhode Island.

Dubois, D. and Prade, H. (1994). A survey of belief revision and updating rules in various uncertainty models. International Journal of Intelligent Systems, 9(1), 61–100.

Duda, R. O., Gasching, J. and Hart, P. E. (1979). Model design in the Prospector consultant system for mineral exploration. In Michie, D. (Ed.), Expert Systems in the Microelectronic Age, pp. 153–167. Edinburgh University Press, Edinburgh, Scotland.

Duda, R. O. and Hart, P. E. (1973). Pattern classification and scene analysis. Wiley, New York.

Duda, R. O., Hart, P. E., and Stork, D. g. (2001). Pattern Classification. Wiley, New York.

Dudek, G. and Jenkin, M. (2000). Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge CB2 2RU, UK.

Durfee, E. H. and Lesser, V. R. (1989). Negotiating task decomposition and allocation using partial global planning. In Huhns, M. and Gasser, L. (Eds.), Distributed AI, Vol. 2. Morgan Kaufmann, San Mateo, California.

Dyer, M. (1983). In-Depth Understanding. MIT Press, Cambridge, Massachusetts.

Dzeroski, S., Muggleton, S. H., and Russell, S. J. (1992). PAC-learnability of determinate logic programs. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (COLT-92), pp. 128–135, Pittsburgh, Pennsylvania. ACM Press.

Earley, J. (1970). An efficient context-free parsing algorithm. Communications of the Association for Computing Machinery, 13(2), 94–102.

Ebeling, C. (1987). All the Right Moves. MIT Press, Cambridge, Massachusetts.

Eco, U. (1979). Theory of Semiotics. Indiana University Press, Bloomington, Indiana.

Edmonds, J. (1965). Paths, trees, and flowers. Canadian Journal of Mathematics, 17, 449–467.

Edwards, P. (Ed.). (1967). The Encyclopedia of Philosophy. Macmillan, London.

Eiter, T., Leone, N., Mateis, C., Pfeifer, G., and Scarcello, F. (1998). The KR system dlv: Progress report, comparisons and benchmarks. In Cohn, A., Schubert, L., and Shapiro, S. (Eds.), Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning, pp. 406–417, Trento, Italy.

Elhadad, M. (1993). FUF: The universal unifier–user manual. Technical report, Ben Gurion University of the Negev, Be’er Sheva, Israel.

Elkan, C. (1993). The paradoxical success of fuzzy logic. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 698–703, Washington, DC. AAAI Press.

Elkan, C. (1997). Boosting and naive Bayesian learning. Tech. rep., Department of Computer Science and Engineering, University of California, San Diego.

Elman, J., Bates, E., Johnson, M., Karmiloff-Smith, A., Parisi, D., and Plunkett, K. (1997). Rethinking Innateness. MIT Press, Cambridge, Massachusetts.

Empson, W. (1953). Seven Types of Ambiguity. New Directions, New York.

Enderton, H. B. (1972). A Mathematical Introduction to Logic. Academic Press, New York.

Erdmann, M. A. and Mason, M. (1988). An exploration of sensorless manipulation. IEEE Journal of Robotics and Automation, 4(4), 369–379.

Erman, L. D., Hayes-Roth, F., Lesser, V. R., and Reddy, R. (1980). The hearsay-ii speech-understanding system: Integrating knowledge to resolve uncertainty. Computing Surveys, 12(2), 213–253.

Ernst, H. A. (1961). MH-1, a Computer-Operated Mechanical Hand. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Ernst, M., Millstein, T., and Weld, D. S. (1997). Automatic SAT-compilation of planning problems. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1169–1176, Nagoya, Japan. Morgan Kaufmann.

Erol, K., Hendler, J., and Nau, D. S. (1994). HTN planning: Complexity and expressivity. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 1123–1128, Seattle. AAAI Press.

Erol, K., Hendler, J., and Nau, D. S. (1996). Complexity results for HTN planning. Annals of Mathematics and Artificial Intelligence, 18(1), 69–93.

Etzioni, O. (1989). Tractable decision-analytic control. In Proc. of 1st International Conference on Knowledge Representation and Reasoning, pp. 114–125, Toronto.

Etzioni, O., Hanks, S., Weld, D. S., Draper, D., Lesh, N., and Williamson, M. (1992). An approach to planning with incomplete information. In Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, Cambridge, Massachusetts.

Etzioni, O. and Weld, D. S. (1994). A softbot-based interface to the Internet. Communications of the Association for Computing Machinery, 37(7), 72–76.

Evans, T. G. (1968). A program for the solution of a class of geometric-analogy intelligence-test questions. In Minsky, M. L. (Ed.), Semantic Information Processing, pp. 271–353. MIT Press, Cambridge, Massachusetts.

Fagin, R., Halpern, J. Y., Moses, Y., and Vardi, M. Y. (1995). Reasoning about Knowledge. MIT Press, Cambridge, Massachusetts.

Fahlman, S. E. (1974). A planning system for robot construction tasks. Artificial Intelligence, 5(1), 1–49.

Fahlman, S. E. (1979). NETL: A System for Representing and Using Real-World Knowledge. MIT Press, Cambridge, Massachusetts.

Faugeras, O. (1992). What can be seen in three dimensions with an uncalibrated stereo rig?. In Sandini, G. (Ed.), Preceedings of the European Conference on Computer Vision, Vol. 588 of Lecture Notes in Computer Science, pp. 563–578. Springer-Verlag.

Faugeras, O. (1993). Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge, Massachusetts.

Faugeras, O., Luong, Q.-T., and Papadopoulo, T. (2001). The Geometry of Multiple Images. MIT Press, Cambridge, Massachusetts.

Fearing, R. S. and Hollerbach, J. M. (1985). Basic solid mechanics for tactile sensing. International Journal of Robotics Research, 4(3), 40–54.

Featherstone, R. (1987). Robot Dynamics Algorithms. Kluwer Academic Publishers, Boston, MA.

Feigenbaum, E. A. (1961). The simulation of verbal learning behavior. Proceedings of the Western Joint Computer Conference, 19, 121–131.

Feigenbaum, E. A., Buchanan, B. G., and Lederberg, J. (1971). On generality and problem solving: A case study using the DENDRAL program. In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 6, pp. 165–190. Edinburgh University Press, Edinburgh, Scotland.

Feigenbaum, E. A. and Feldman, J. (Eds.). (1963). Computers and Thought. McGraw-Hill, New York.

Feldman, J. and Sproull, R. F. (1977). Decision theory and artificial intelligence II: The hungry monkey. Technical report, Computer Science Department, University of Rochester.

Feldman, J. and Yakimovsky, Y. (1974). Decision theory and artificial intelligence I: Semantics-based region analyzer. Artificial Intelligence, 5(4) 349–371.

Fellbaum, C. (2001). Wordnet: An Electronic Lexical Database. MIT Press, Cambridge, Massachusetts.

Feller, W. (1971). An Introduction to Probability Theory and its Applications, Vol. 2. John Wiley.

Ferraris, P. and Giunchiglia, E. (2000). Planning as satisability in nondeterministic domains. In Proceedings of Seventeenth National Conference on Artificial Intelligence, pp. 748–753. AAAI Press.

Fikes, R. E., Hart, P. E., and Nilsson, N. J. (1972). Learning and executing generalized robot plans. Artificial Intelligence, 3(4), 251–288.

Fikes, R. E. and Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3–4), 189–208.

Fikes, R. E. and Nilsson, N. J. (1993). STRIPS, a retrospective. Artificial Intelligence, 59(1–2), 227–232.

Findlay, J. N. (1941). Time: A treatment of some puzzles. Australasian Journal of Psychology and Philosophy, 19(3), 216–235.

Finney, D. J. (1947). Probit analysis: A statistical treatment of the sigmoid response curve. Cambridge University Press, Cambridge, UK.

Firby, J. (1994). Task networks for controlling continuous processes. In Hammond, K. (Ed.), Proceedings of the Second International Conference on AI Planning Systems, pp. 49–54, Menlo Park, CA. AAAI Press.

Firby, R. J. (1996). Modularity issues is reactive planning. In Proceedings of the 3rd International Conference on Artificial Intelligence Planning Systems (AIPS-96), pp. 78–85, Edinburgh, Scotland. AAAI Press.

Fischer, M. J. and Ladner, R. E. (1977). Propositional modal logic of programs. In Proceedings of the 9th ACM Symposium on the Theory of Computing, pp. 286–294, New York. ACM Press.

Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A 222, 309–368.

Fix, E. and Hodges, J. L. (1951). Discriminatory analysis–nonparametric discrimination: Consistency properties. Tech. rep. 21-49-004, USAF School of Aviation Medicine, Randolph Field, Texas.

Fogel, D. B. (2000). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, New Jersey.

Fogel, L. J., Owens, A. J., and Walsh, M. J. (1966). Artificial Intelligence through Simulated Evolution. Wiley, New York.

Forbes, J. (2002). Learning Optimal Control for Autonomous Vehicles. Ph.D. thesis, University of California, Berkeley.

Forbus, K. D. (1985). The role of qualitative dynamics in naive physics. In Hobbs, J. R. and Moore, R. C. (Eds.), Formal Theories of the Commonsense World, chap. 5, pp. 185–226. Ablex Norwood, New Jersey.

Forbus, K. D. and de Kleer, J. (1993). Building Problem Solvers. MIT Press, Cambridge, Massachusetts.

Ford, K. M. and Hayes, P. J. (1995). Turing Test considered harmful. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), pp. 972–977, Montreal. Morgan Kaufmann.

Forestier, J.-P. and Varaiya, P. (1978). Multilayer control of large Markov chains. IEEE Transactions on Automatic Control, 23(2), 298–304.

Forgy, C. (1981). OPS5 user’s manual. Technical report CMU-CS-81-135, Computer Science Department, Carnegie-Mellon University, Pittsburgh.

Forgy, C. (1982). A fast algorithm for the many patterns/many objects match problem. Artificial Intelligence, 19(1), 17–37.

Forsyth, D. and Zisserman, A. (1991). Reflections on shading. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 13(7), 671–679.

Fortescue, M. D. (1984). West Greenlandic. Croom Helm, London.

Foster, D. W. (1989). Elegy by W. W.: A Study in Attribution. Associated University Presses, Cranbury, New Jersey.

Fourier, J. (1827). Analyse des travaux de l’Académie Royale des Sciences, pendant l’année 1824; partie mathématique. Histoire de l’Académie Royale des Sciences de France, 7, xlvii–lv.

Fox, D., Burgard, W., Dellaert, F., and Thrun, S. (1999). Monte Carlo localization: Efficient position estimation for mobile robots. In Proceedings of the National Conference on Artificial Intelligence (AAAI), Orlando, FL. AAAI.

Fox, M. S. (1990). Constraint-guided scheduling: A short history of research at CMU. Computers in Industry, 14(1–3), 79–88.

Fox, M. S., Allen, B., and Strohm, G. (1982). Job shop scheduling: An investigation in constraint-directed reasoning. In Proceedings of the National Conference on Artificial Intelligence (AAAI-82), pp. 155–158, Pittsburgh, Pennsylvania. Morgan Kaufmann.

Fox, M. S. and Long, D. (1998). The automatic inference of state invariants in TIM. Journal of Artificial Intelligence Research, 9, 367–421.

Frakes, W. and Baeza-Yates, R. (Eds.). (1992). Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Upper Saddle River, New Jersey.

Francis, S. and Kucera, H. (1967). Computing Analysis of Present-day American English. Brown University Press, Providence, Rhode Island.

Franco, J. and Paull, M. (1983). Probabilistic analysis of the Davis Putnam procedure for solving the satisfiability problem. Discrete Applied Mathematics, 5, 77–87.

Frank, R. H. and Cook, P. J. (1996). The Winner-Take-All Society. Penguin, New York.

Frege, G. (1879). Begriffsschrift, eine der arithmetischen nachgebildete Formelsprache des reinen Denkens. Halle, Berlin. English translation appears in van Heijenoort (1967).

Freuder, E. C. (1978). Synthesizing constraint expressions. Communications of the Association for Computing Machinery, 21(11), 958–966.

Freuder, E. C. (1982). A sufficient condition for backtrack-free search. Journal of the Association for Computing Machinery, 29(1), 24–32.

Freuder, E. C. (1985). A sufficient condition for backtrack-bounded search. Journal of the Association for Computing Machinery, 32(4), 755–761.

Freuder, E. C. and Mackworth, A. K. (Eds.). (1994). Constraint-based reasoning. MIT Press, Cambridge, Massachusetts.

Freund, Y. and Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy. Morgan Kaufmann.

Friedberg, R. M. (1958). A learning machine: Part I. IBM Journal, 2, 2–13.

Friedberg, R. M., Dunham, B., and North, T. (1959). A learning machine: Part II. IBM Journal of Research and Development, 3(3), 282–287.

Friedman, G. J. (1959). Digital simulation of an evolutionary process. General Systems Yearbook, 4, 171–184.

Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28(2), 337–374.

Friedman, N. (1998). The Bayesian structural EM algorithm. In Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference, Madison, Wisconsin. Morgan Kaufmann.

Friedman, N. and Goldszmidt, M. (1996). Learning Bayesian networks with local structure. In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference, pp. 252–262, Portland, Oregon. Morgan Kaufmann.

Fry, D. B. (1959). Theoretical aspects of mechanical speech recognition. Journal of the British Institution of Radio Engineers, 19(4), 211–218.

Fuchs, J. J., Gasquet, A., Olalainty, B., and Currie, K. W. (1990). PlanERS-1: An expert planning system for generating spacecraft mission plans. In First International Conference on Expert Planning Systems, pp. 70–75, Brighton, UK. Institute of Electrical Engineers.

Fudenberg, D. and Tirole, J. (1991). Game theory. MIT Press, Cambridge, Massachusetts.

Fukunaga, A. S., Rabideau, G., Chien, S., and Yan, D. (1997). ASPEN: A framework for automated planning and scheduling of spacecraft control and operations. In Proceedings of the International Symposium on AI, Robotics and Automation in Space, pp. 181–187, Tokyo.

Fung, R. and Chang, K. C. (1989). Weighting and integrating evidence for stochastic simulation in Bayesian networks. In Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI-89), pp. 209–220, Windsor, Ontario. Morgan Kaufmann.

Gaifman, H. (1964). Concerning measures in first order calculi. Israel Journal of Mathematics, 2, 1–18.

Gallaire, H. and Minker, J. (Eds.). (1978). Logic and Databases. Plenum, New York.

Gallier, J. H. (1986. Logic for Computer Science: Foundations of Automatic Theorem Proving. Harper and Row, New York.

Gallo, G. and Pallottino, S. (1988). Shortest path algorithms. Annals of Operations Research, 13, 3–79.

Gamba, A., Gamberini, L., Palmieri, G., and Sanna, R. (1961). Further Experiments with PAPA. Nuovo Cimento Supplemento, 20(2), 221–231.

Garding, J. (1992). Shape from texture for smooth curved surfaces in perspecitve projection. Journal of Mathematical Imaging and Vision, 2(4), 327–350.

Gardner, M. (1968). Logic Machines, Diagrams and Boolean Algebra. Dover, New York.

Garey, M. R. and Johnson, D. S. (1979). Computers and Intractability. W. H. Freeman, New York.

Gaschnig, J. (1977). A general backtrack algorithm that eliminates most redundant tests. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence (IJCAI-77), p. 457, Cambridge, Massachusetts. IJCAII.

Gaschnig, J. (1979.) Performance measurement and analysis of certain search algorithms. Technical report CMU-CS-79-124, Computer Science Department, Carnegie-Mellon University.

Gasser, R., (1995). Efficiently harnessing computational resources for exhaustive search. Ph.D. thesis, ETH Zürich, Switzerland.

Gasser, R. (1998). Solving nine men’s morris. In Nowakowski, r. (Ed.), Games of No Chance. Cambridge University Press, Cambridge, UK.

Gat, E. (1998). Three-layered architectures. In Kortenkamp, D., Bonasso, R. P., and Murphy, R. (Eds.), AI-based Mobile Robots: Case Studies of Successful Robot Systems, pp. 195–210. MIT Press.

Gauss, K. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Sumtibus F. Perthes et I. H. Besser, Hamburg.

Gauss, K. F. (1829). Beiträge zur theorie der algebraischen gleichungen. Collected in Werke, Vol. 3, pages 71–102. K. Gesellschaft Wissenschaft, Göttingen, Germany, 1876.

Gawande, A. (2002). Complications: A Surgeon’s Notes on an Imperfect Science. Metropolitan Books, New York.

Ge, N., Hale, J. and Charniak, E. (1998). A statistical approach to anaphora resolution. In Proceedings of the Sixth Workshop on Very Large Corpora, pp. 161–171, Montreal. COLING-ACL.

Geiger, D., Verma, T., and Pearl, J. (1990). Identifying independence in Bayesian networks. Networks, 20(5), 507–534.

Gelb, A. (1974). Applied Optimal Estimation. MIT Press, Cambridge, Massachusetts.

Gelernter, H. (1959). Realization of a geometry-theorem proving machine. In Proceedings of an International Conference on Information Processing, pp. 273–282, Paris. UNESCO House.

Gelfond, M. and Lifschitz, v. (1988). Compiling circumscriptive theories into logic programs. In Reinfrank, M., de Kleer, J., Ginsberg, M. L., and Sandewall, E. (Eds.), Non-Monotonic Reasoning: 2nd International Workshop Proceedings, pp. 74–99, Grassau, Germany. Springer-Verlag.

Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. (1995). Bayesian Data Analysis. Chapman & Hall, London.

Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 6(6), 721–741.

Genesereth, M. R. (1984). The use of design descriptions in automated diagnosis. Artificial Intelligence, 24(1–3), 411–436.

Genesereth, M. R. and Nilsson, N. J. (1987). Logical Foundations of Artificial Intelligence. Morgan Kaufmann, San Mateo, California.

Genesereth, M. R. and Nourbakhsh, I. (1993). Time-saving tips for problem solving with incomplete information. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 724–730, Washington, DC. AAAI Press.

Genesereth, M. R. and Smith, D. E. (1981). Metalevel architecture. Memo HPP-81-6, Computer Science Department, Stanford University, Stanford, California.

Gentner, D. (1983). Structure mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.

Gentzen, G. (1934). Untersuchungen über das logische Schliessen. Mathematische Zeitschrift, 39, 176–210, 405–431.

Georgeff, M. P. and Lansky, A. L. (1987). Reactive reasoning and planning. In Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI-87), pp. 677–682, Seattle. Morgan Kaufmann.

Gerevini, A. and Schubert, L. K. (1996). Accelerating partial-order planners: Some techniques for effective search control and pruning. Journal of Artificial Intelligence Research, 5, 95–137.

Gerevini, A. and Serina, I. (2002). LPG: A planner based on planning graphs with action costs. In Proceedings of the Sixth International Conference on AI Planning and Scheduling, pp. 281–290, Menlo Park, California. AAAI Press.

Germann, U., Jahr, M., Knight, K., Marcu, D., and Yamada, K. (2001). Fast decoding and optimal decoding for machine translation. In Proceedings of the Conference of the Association for Computational Linguistics (ACL), pp. 228–235, Toulouse, France.

Gershwin, G. (1937). Let’s call the whole thing off. song.

Ghahramani, Z. and Jordan, M. I. (1997). Factorial hidden Markov models. Machine Learning, 29, 245–274.

Ghallab, M., Howe, A., Knoblock, C. A., and McDermott, D. (1998). PDDL–the planning dormain definition language. Tech. rep. DCS TR-1165, Yale Center for Computational Vision and Control, New Haven, Connecticut.

Ghallab, M. and Laruelle, H. (1994). Representation and control in IxTeT, a temporal planner. In Proceedings of the 2nd International Conference on Artificial Intelligence Planning Systems (AIPS-94), pp. 61–67, Chicago. AAAI Press.

Giacomo, G. D., Lespérance, Y., and Levesque, H. J. (2000). ConGolog, a concurrent programming language based on the situation calculus. Artificial Intelligence, 121, 109–169.

Gibson, J. J. (1950). The Perception of the Visual World. Houghton Mifflin, Boston.

Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Houghton Mifflin, Boston.

Gibson, J. J., Olum, P., and Rosenblatt, F. (1955). Parallax and perspective during aircraft landings. American Journal of Psychology, 68, 372–385.

Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (Eds.). (1996). Markov chain Monte Carlo in practice. Chapman and Hall, London.

Gilks, W. R., Thomas, A., and Spiegelhalter, D. J. (1994). A language and program for complex Bayesian modelling. The Statistician, 43, 169–178.

Gilmore, P. C. (1960). A proof method for quantification theory: Its justification and realization. IBM Journal of Research and Development, 4, 28–35.

Ginsberg, M. L. (1989). Universal planning: An (almost) universally bad idea. AI Magazine, 10(4), 40–44.

Ginsberg, M. L. (1993). Essentials of Artificial Intelligence. Morgan Kaufmann, San Mateo, California.

Ginsberg, M. L. (1999). GIB: Steps toward an expert-level bridge-playing program. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 584–589, Stockholm. Morgan Kaufmann.

Ginsberg, M. L., Frank, M., Halpin, M. P., and Torrance, M. C. (1990). Search lessons learned from crossword puzzles. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Vol. 1, pp. 210–215, Boston. MIT Press.

Gittins, J. C. (1989). Multi-Armed Bandit Allocation Indices. Wiley, New York.

Glanc, A. (1978). On the etymology of the word „robot”. SIGART Newsletter, 67, 12.

Glover, F. (1989). Tabu search: 1. ORSA Journal on Computing, 1(3), 190–206.

Glover, F. and Laguna, M. (Eds.). (1997). Tabu search. Kluwer, Dordrecht, Netherlands.

Gödel, K. (1930). Über die Vollständigkeit des Logikkalküls, Ph.D. thesis, University of Vienna.

Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik, 38, 173–198.

Goebel, J., Volk, K., Walker, H., and Gerbault, F. (1989). Automatic classification of spectra from the infrared astronomical satellite (IRAS). Astronomy and Astrophysics, 222, L5–L8.

Gold, B. and Morgan, N. (2000). Speech and Audio Signal Processing. Wiley, New York.

Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447–474.

Golden, K. (1998). Leap before you look: Information gathering in the PUCCINI planner. In Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems (AIPS-98), pp. 70–77, Pittsburgh, Pennsylvania. AAAI Press.

Goldman, N. (1975). Conceptual generation. In Schank, R. (Ed.), Conceptual Information Processing, chap. 6. North-Holland, Amsterdam.

Goldman, R. and Boddy, M. (1996). Expressive planning and explicit knowledge. In Proceedings of the 3rd International Conference on Artificial Intelligence Planning Systems (AIPS-96), pp. 110–117, Edinburgh, Scotland. AAAI Press.

Gomes, C., Selman, B., and Kautz, H. (1998). Boosting combinatorial search through randomization. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 431–437, Madison, Wisconsin. AAAI Press.

Good, I. J. (1950). Contribution to the discussion of Eliot Slater’s „Statistics for the chess computer and the factor of mobility”. In Symposium on Information Theory, p. 199, London. Ministry of Supply.

Good, I. J. (1961). A causal calculus. British Journal of the Philosophy of Science, 11, 305–318.

Good, I. J. (1965). Speculations concerning the first ultraintelligent machine. In Alt, F. L. and Rubinoff, M. (Eds.), Advances in Computers, Vol. 6, pp. 31–88. Academic Press, New York.

Goodman, D. and Keene, R. (1997). Man versus Machine: Kasparov versus Deep Blue. H3 Publications, Cambridge, Massachusetts.

Goodman, N. (1954). Fact, Fiction and Forecast. University of London Press, London.

Goodman, N. (1977). The Structure of Appearance (3rd edition). D. Reidel, Dordrecht, Netherlands.

Gordon, M. J., Milner, A. J., and Wadsworth, C. P. (1979). Edinburgh LCF. Springer-Verlag, Berlin.

Gordon, N. J. (1994). Bayesian methods for tracking. Ph.D. thesis, Imperial College, University of London.

Gordon, N. J., Salmond, D. J., and Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.

Gorry, G. A. (1968). Strategies for computer-aided diagnosis. Mathematical Biosciences, 2(3–4, 293–318.

Gorry, G. A., Kassirer, J. P., Essig, A., and Schwartz, W. B. (1973). Decision analysis as the basis for computer-aided management of acute renal failure. American Journal of Medicine, 55, 473–484.

Gottlob, G., Leone, N., and Scarcello, F. (1999a). A comparison of structural CSP decomposition methods. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 394–399, Stockholm. Morgan Kaufmann.

Gottlob, G., Leone, N., and Scarcello, F. (1999b). Hypertree decompositions and tractable queries. In Proceedings of the 18th ACM International Symposium on Principles of Database Systems, pp. 21–32, Philadelphia. Association for Computing Machinery.

Graham, S. L., Harrison, M. A., and Ruzzo, W. L. (1980). An improved context-free recognizer. ACM Transactions on Programming Languages and Systems, 2(3), 415–462.

Grassmann, H. (1861). Lehrbuch der Arithmetik. Th. Chr. Fr. Enslin, Berlin.

Grayson, C. J. (1960). Decisions under uncertainty: Drilling decisions by oil and gas operators. Tech. rep., Division of Research, Harvard Business School, Boston.

Green, B., Wolf, A., Chomsky, C., and Laugherty, K. (1961). BASEBALL: An automatic question answerer. In Proceedings of the Western Joint Computer Conference, pp. 219–224.

Green, C. (1969a). Application of theorem proving to problem solving. In Proceedings of the First International Joint Conference on Artificial Intelligence (ijcai-69), pp. 219–239, Washington, DC. IJCAII.

Green, C. (1969b). Theorem-proving by resolution as a basis for question-answering systems. In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 183–205. Edinburgh University Press, Edinburgh, Scotland.

Green, C. and Raphael, B. (1968). The use of theorem-proving techniques in question-answering systems. In Proceedings of the 23rd ACM National Conference, Washington, DC. ACM Press.

Greenblatt, R. D., Eastlake, D. E., and Crocker, S. D. (1967). The Greenblatt chess program. In Proceedings of the Fall Joint Computer Conference, pp. 801–810. American Federation of Information Processing Societies (AFIPS).

Greiner, R. (1989). Towards a formal analysis of EBL. In Proceedings of the Sixth International Machine Learning Workshop, pp. 450–453, Ithaca, NY. Morgan Kaufmann.

Grice, H. P. (1957). Meaning. Philosophical Review, 66, 377–388.

Grosz, B. J., Joshi, A. K., and Weinstein, S. (1995). Centering: A framework for modeling the local coherence of discourse. Computational Linguistics, 21(2), 203–225.

Grosz, B. J. and Sidner, C. L. (1986). Attention, intentions, and the structure of discourse. Computational Linguistics, 12(3), 175–204.

Grosz, B. J., Sparck Jones, K., and Webber, B. L. (Eds.). (1986). Readings in Natural Language Processing. Morgan Kaufmann, San Mateo, California.

Grove, W. and Meehl, P. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical statistical controversy. Psychology, Public Policy, and Law, 2, 293–323.

Gu, J. (1989). Parallel Algorithms and Architectures for Very Fast AI Search. Ph.D. thesis, University of Utah.

Guard, J., Oglesby, F., Bennett, J., and Settle, L. (1969). Semi-automated mathematics. Journal of the Association for Computing Machinery, 16, 49–62.

Guibas, L. J., Knuth, D. E., and Sharir, M. (1992). Randomized incremental construction of Delaunay and Voronoi diagrams. Algorithmica, 7, 381–413. See also 17th Int. Coll. on Automata, Languages and Programming, 1990, pp. 414–431.

Haas, A. (1986). A syntactic theory of belief and action. Artificial Intelligence, 28(3), 245–292.

Hacking, I. (1975). The Emergence of Probability. Cambridge University Press, Cambridge, UK.

Hald, A. (1990). A History of Probability and Statistics and Their Applications before 1750. Wiley, New York.

Halpern, J. Y. (1990). An analysis of first-order logics of probability. Artificial Intelligence, 46(3), 311–350.

Hamming, R. W. (1991). The Art of Probability for Scientists and Engineers. Addison-Wesley, Reading, Massachusetts.

Hammond, K. (1989). Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, New York.

Hamscher, W., Console, L., and Kleer, J. D. (1992). Readings in Model-based Diagnosis. Morgan Kaufmann, San Mateo, California.

Handschin, J. E. and Mayne, D. Q. (1969). Monte Carlo techniques to estimate the conditional expectation in multi-stage nonlinear filtering. International Journal of Control, 9(5), 547–559.

Hansen, E. (1998). Solving POMDPs by searching in policy space. In Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference, pp. 211–219, Madison, Wisconsin. Morgan Kaufmann.

Hansen, E. and Zilberstein, S. (2001). LAO*: a heuristic search algorithm that finds solutions with loops. Artificial Intelligence, 129(1–2), 35–62.

Hansen, P. and Jaumard, B. (1990). Algorithms for the maximum satisfiability problem. Computing, 44(4), 279–303.

Hanski, I. and Cambefort, Y. (Eds.). (1991). Dung Beetle Ecology. Princeton University Press, Princeton, New Jersey.

Hansson, O. and Mayer, A. (1989). Heuristic search as evidential reasoning. In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, Ontario. Morgan Kaufmann.

Hansson, O., Mayer, A., and Yung, M. (1992). Criticizing solutions to relaxed models yields powerful admissible heuristics. Information Sciences, 63(3), 207–227.

Haralick, R. M. and Elliot, G. L. (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14(3), 263–313.

Hardin, G. (1968). The tragedy of the commons. Science, 162, 1243–1248.

Harel, D. (1984). Dynamic logic. In Gabbay, D. and Guenthner, F. (Eds.), Handbook of Philosophical Logic, Vol. 2, pp. 497–604. D. Reidel, Dordrecht, Netherlands.

Harman, G. H. (1983). Change in View: Principles of Reasoning. MIT Press, Cambridge, Massachusetts.

Harsanyi, J. (1967). Games with incomplete information played by Bayesian players. Management Science, 14, 159–182.

Hart, P. E., Nilsson, N. J. and Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, SSC-4(2), 100–107.

Hart, P. E., Nilsson, N. J., and Raphael, B. (1972). Correction to „A formal basis for the heuristic determination of minimum cost paths”. SIGART Newsletter, 37, 28–29.

Hart, T. P. and Edwards, D. J. (1961). The tree prune (TP) algorithm. Artificial intelligence project memo 30, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Hartley, R. and Zisserman, A. (2000). Multiple view geometry in computer vision. Cambridge University Press, Cambridge, UK.

Haslum, P. and Geffner, H. (2001). Heuristic planning with time and resources. In Proceedings of the IJCAI-01 Workshop on Planning with Resources, Seattle.

Hastie, T. and Tibshirani, R. (1996). Discriminant adaptive nearest neighbor classification and regression. In Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E. (Eds.), Advances in Neural Information Processing Systems, Vol. 8, pp. 409–15. MIT Press, Cambridge, Massachusetts.

Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer-Verlag, Berlin.

Haugeland, J. (Ed.). (1981). Mind Design. MIT Press, Cambridge, Massachusetts.

Haugeland, J. (Ed.). (1985). Artificial Intelligence: The Very Idea. MIT Press, Cambridge, Massachusetts.

Haussler, D. (1989). Learning conjunctive concepts in structural domains. Machine Learning, 4(1), 7–40.

Havelund, K., Lowry, M., Park, S., Pecheur, C., Penix, J., Visser, W., and White, J. L. (2000). Formal analysis of the remote agent before and after flight. In Proceedings of the 5th NASA Langley Formal Methods Workshop, Williamsburg, VA.

Hayes, P. J. (1978). The naive physics manifesto. In Michie, D. (Ed.), Expert Systems in the Microelectronic Age. Edinburgh University Press, Edinburgh, Scotland.

Hayes, P. J. (1979). The logic of frames. In Metzing, D. (Ed.), Frame Conceptions and Text Understanding, pp. 46–61. de Gruyter, Berlin.

Hayes, P. J. (1985a). Naive physics I: Ontology for liquids. In Hobbs, J. R. and Moore, R. C. (eds.), Formal Theories of the Commonsense World, chap. 3, pp. 71–107. Ablex, Norwood, New Jersey.

Hayes, P. J. (1985b). The second naive physics manifesto. In Hobbs, J. R. and Moore, R. C. (Eds.), Formal Theories of the Commonsense World, chap. 1, pp. 1–36. Ablex, Norwood, New Jersey.

Hebb, D. O. (1949). The Organization of Behavior. Wiley, New York.

Heckerman, D. (1986). Probabilistic interpretation for MYCIN’s certainty factors. In Kanal, L. N. and Lemmer, J. F. (Eds.), Uncertainty in Artificial Intelligence, pp. 167–196. Elsevier/North-Holland, Amsterdam, London, New York.

Heckerman, D. (1991). Probabilistic Similarity Networks. MIT Press, Cambridge, Massachusetts.

Heckerman, D. (1998). A tutorial on learning with Bayesian networks. In Jordan, M. I. (Ed.), Learning in graphical models. Kluwer, Dordrecht, Netherlands.

Heckerman, D., Geiger, D., and Chickering, D. M. (1994). Learning Bayesian networks: The combination of knowledge and statistical data. Technical report MSR-TR-94-09, Microsoft Research, Redmond, Washington.

Heim, I. and Kratzer, A. (1998). Semantics in a Generative Grammar. Blackwell, Oxford, UK.

Heinz, E. A. (2000). Scalable search in computer chess. Vieweg, Braunschweig, Germany.

Held, M. and Karp, R. M. (1970). The traveling salesman problem and minimum spanning trees. Operations Research, 18, 1138–1162.

Helmert, M. (2001). On the complexity of planning in transportation domains. In Cesta, A. and Barrajo, D. (Eds.), Sixth European Conference on Planning (ECP-01), Toledo, Spain. Springer-Verlag.

Hendrix, G. G. (1975). Expanding the utility of semantic networks through partitioning. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence (IJCAI-75), pp. 115–121, Tbilisi, Georgia. IJCAII.

Henrion, M. (1988). Propagation of uncertainty in Bayesian networks by probabilistic logic sampling. In Lemmer, J. F. and Kanal, L. N. (Eds.), Uncertainty in Artificial Intelligence 2, pp. 149–163. Elsevier/North-Holland, Amsterdam, London, New York.

Henzinger, T. A. and Sastry, S. (Eds.). (1998). Hybrid systems: Computation and control. Springer-Verlag, Berlin.

Herbrand, J. (1930). Recherches sur la Théorie de la Démonstration. Ph.D. thesis, University of Paris.

Hewitt, C. (1969). PLANNER: a language for proving theorems in robots. In Proceedings of the First International Joint Conference on Artificial Intelligence (IJCAI-69), pp. 295–301, Washington, DC. IJCAII.

Hierholzer, C. (1873). Über die Möglichkeit, einen Linienzug ohne Wiederholung und ohne Unterbrechung zu umfahren. Mathematische Annalen, 6, 30–32.

Hilgard, E. R. and Bower, G. H. (1975). Theories of Learning (4th edition). Prentice-Hall, Upper Saddle River, New Jersey.

Hintikka, J. (1962). Knowledge and Belierf. Cornell University Press, Ithaca, New York.

Hinton, G. E. and Anderson, J. A. (1981). Parallel Models of Associative Memory. Lawrence Erlbaum Associates, Potomac, Maryland.

Hinton, G. E. and Nowlan, S. J. (1987). How learning can guide evolution. Complex Systems, 1(3), 495–502.

Hinton, G. E. and Sejnowski, T. (1983). Optimal perceptual inference. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 448–453, Washington, DC. IEEE Computer Society Press.

Hinton, G. E. and Sejnowski, T. (1986). Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L. (Eds.), Parallel Distributed Processing, chap. 7, pp. 282–317. MIT Press, Cambridge, Massachusetts.

Hirsh, H. (1987). Explanation-based generalization in a logic programming environment. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), Milan. Morgan Kaufmann.

Hirst, G. (1981). Anaphora in Natural Language Understanding: A Survey, Vol. 119 of Lecture Notes in Computer Science. Springer-Verlag, Berlin.

Hirst, G. (1987). Semantic Interpretation against Ambiguity. Cambridge University Press, Cambridge, UK.

Hobbs, J. R. (1978). Resolving pronoun references. Lingua, 44, 339–352.

Hobbs, J. R. (1990). Literature and Cognition. CSLI Press, Stanford, California.

Hobbs, J. R., Appelt, D., Bear, J., Israel, D., Kameyama, M., Stickel, M. E., and Tyson, M. (1997). FASTUS: A cascaded finite-state transducer for extracting information from natural-language text. In Roche, E. and Schabes, Y. (Eds.) Finite-State Devices for Natural Language Processing, pp. 383–406. MIT Press, Cambridge, Massachusetts.

Hobbs, J. R. and Moore, R. C. (Eds.). (1985). Formal Theories of the Commonsense World. Ablex, Norwood, New Jersey.

Hobbs, J. R., Stickel, M. E., Appelt, D., and Martin, P. (1993). Interpretation as abduction. Artificial Intelligence, 63(1–2), 69–142.

Hoffmann, J. (2000). A heuristic for domain independent planning and its use in a n enforced hillclimbing algorithm. In Proceedings of the 12th International Symposium on Methodologies for Intelligent Systems, pp. 216–227, Charlotte, North Carolina. Springer-Verlag.

Hogan, N. (1985). Impedance control: An approach to manipulation. parts i, ii, and iii. Transactions ASME Journal of Dynamics, Systems, Measurement, and Control, 107(3), 1–24.

Holland, J. H. (1975). Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan.

Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Addison-Wesley, Reading, Massachusetts.

Holldobler, S. and Schneeberger, J. (1990). A new deductive approach to planning. New Generation Computing, 8(3), 225–244.

Holzmann, G. J. (1997). The Spin model checker. ISSS Transactions on Software Engineering, 23(5), 279–295.

Hood, A. (1824). Case 4th–28 July 1824 (Mr. Hood’s cases of injuries of the brain). The Phrenological Journal and Miscellany, 2, 82–94.

Hopfield, J. J. (1982). Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences of the United States of America, 79, 2554–2558.

Horn, A. (1951). On sentences which are true of direct unions of algebras. Journal of Symbolic Logic, 16, 14–21.

Horn, B. K. P. (1970). Shape from shading: A method for obtaining the shape of a smooth opeque object from one view. Technical report 232, MIT Artificial Intelligence Laboratory, Cambridge, Massachusetts.

Horn, B. K. P. (1986). Robot Vision. MIT Press, Cambridge, Massachusetts.

Horn, B. K. P. and Brooks, M. J. (1989). Shape from Shading. MIT Press, Cambridge, Massachusetts.

Horning, J. J. (1969). A study of grammatical inference. Ph.D. thesis, Stanford University.

Horowitz, E. and Sahni, s. (1978). Fundamentals of computer algorithms. Computer Science Press, Rockwille, Maryland.

Horswill, I. (2000. Functional programming of behavior-based systems. Autonomous Robots, 9, 83–93.

Horvitz, E. J. (1987). Problem-solving design: Reasoning about computational value, trade-offs, and resources. In Proceedings of the Second Annual NASA Research Forum, pp. 26–43, Moffett Field, California. NASA Ames Research Center.

Horvitz, E. J. (1989). Rational metareasoning and compilation for optimizing decisions under bounded resources. In Proceedings of Computational Intelligence 89, Milan. Association for Computing Machinery.

Horvitz, E. J. and Barry, M. (1995). Display of information for time-critical decision making. In Uncertainty in Artificial Intelligence: Proceedings of the Eleventh Conference, pp. 296–305, Montreal Canada. Morgan Kaufmann.

Horvitz, E. J., Breese, J. S., Heckerman, D., and Hovel, D. (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference, pp. 256–265, Madison, Wisconsin. Morgan Kaufmann.

Horvitz, E. J., Breese, J. S., and Henrion, M. (1988). Decision theory in expert systems and artificial intelligence. International Journal of Approximate Reasoning, 2, 247–302.

Horvitz, E. J. and Breese, J. S. (1996). Ideal partition of resources for metareasoning. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 1229–1234, Portland, Oregon. AAAI Press.

Horvitz, E. J. and Heckerman, D. (1986). The inconsistent use of measures of certainty in artificial intelligence research. In Kanal, L. N. and Lemmer, J. F. (Eds.), Uncertainty in Artificial Intelligence, pp. 137–151. Elsevier/North-Holland, Amsterdam, London, New York.

Horvitz, E. J., Heckerman, D., and Langlotz, C. P. (1986). A framework for comparing alternative formalisms for plausible reasoning. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), Vol. 1, pp. 210–214, Philadelphia. Morgan Kaufmann.

Hovy, E. (1988). Generating Natural Language under Progmatic Constraints. Lawrence Erlbaum, Potomac, Maryland.

Howard, R. A. (1960). Dynamic Programming and Markov Processes. MIT Press, Cambridge, Massachusetts.

Howard, R. A. (1966). Information value theory. IEEE Transactions on Systems Science and Cybernetics, SSC-2, 22–26.

Howard, R. A. (1977). Risk preference. In Howard, R. A. and Matheson, J. E. (Eds.), Readings in Decision Analysis, pp. 429–465. Decision Analysis Group, SRI International, Menlo Park, California.

Howard, R. A. (1989). Microrisks for medical decision analysis. International Journal of Technology Assessment in Health Care, 5, 357–370.

Howard, R. A. and Matheson, J. e. (1984). Influence diagrams. In Howard, R. A. and Matheson, J. E. (Eds.), Readings on the Principles and Applications of Decision Analysis, pp. 721–762. Strategic Decisions Group, Menlo Park, California.

Hsu, F.-H. (1999). IBM’s Deep Blue chess grandmaster chips. IEEE Micro, 19(2), 70–80.

Hsu, F.-H., Anantharaman, T. S., Campbell, M. S., and Nowatzyk, A. (1990). A grandmaster chess machine. Scientific American, 263(4), 44–50.

Huang, T., Koller, D., Malik, J., Ogasawara, G., Rao, B., Russell, S. J., and Weber, J. (1994). Automatic symbolic traffic scene analysis using belief networks. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 966–972, Seattle. AAAI Press.

Huang, X. D., Acero, A., and Hon, H. (2001). Spoken Language Processing. Prentice Hall, Upper Saddle River, New Jersey.

Hubel, D. H. (1988). Eye, Brain, and Vision. W. H. Freeman, New York.

Huddleston, R. D. and Pullum, G. K. (2002). The Cambridge Grammar of the English Language. Cambridge University Press, Cambridge, UK.

Huffman, D. A. (1971). Impossible objects as nonsense sentences. In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 6, pp. 295–324. Edinburgh University Press, Edinburgh, Scotland.

Hughes, B. D. (1995). Random Walks and Random Environments, Vol. 1: Random Walks. Oxford University Press, Oxford, UK.

Huhns, M. N. and Singh, M. P. (Eds.). (1998). Readings in agents. Morgan Kaufmann, San Mateo, California.

Hume, D. (1739). A Treatise of Human Nature (2nd edition). Republished by Oxford University Press, 1978, Oxford, UK.

Hunsberger, L. and Grosz, B. J. (2000). A combinatorial auction for collaborative planning. In International Conference on Multi-Agent Systems (ICMAS-2000).

Hunt, E. B., Marin, J., and Stone, P. T. (1966). Experiments in Induction. Academic Press, New York.

Hunter, L. and States, D. J. (1992). Bayesian classification of protein structure. IEEE Expert, 7(4), 67–75.

Hurwicz, L. (1973). The design of mechanisms for resource allocation. American Economic Review Papers and Proceedings, 63(1), 1–30.

Hutchins, W. J. and Somers, H. (1992). An Introduction to Machine Translation. Academic Press, New York.

Huttenlocher, D. P. and Ullman, S. (1990). Recognizing solid objects by alignment with an image. International Journal of Computer Vision, 5(2), 195–212.

Huygens, C. (1657). Ratiociniis in ludo aleae. In van Schooten, F. (Ed.), Exercitionum Mathematicorum. Elsevirii, Amsterdam.

Hwa, R. (1998). An empirical evaluation of probabilistic lexicalized tree insertion grammars. In Proceedings of COLING-ACL ’98, pp. 557–563, Montreal. International Committee on Computational Linguistics and Association for Computational Linguistics.

Hwang, C. H. and Schubert, L. K. (1993). EL: A formal, yet natural, comprehensive knowledge representation. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 676-682, Washington, DC. AAAI Press.

Indyk, P. (2000). Dimensionality reduction techniques for proximity problems. In Proceedings of the Eleventh Annual ACM–SIAM Symposium on Discrete Algorithms, pp. 371–378, San Francisco. Asociation for Computing Machinery.

Ingerman, P. Z. (1967). Panini–Backus form suggested. Communications of the Association for Computing Machinery, 10(3), 137.

Inoue, K. (2001). Inverse entailment for full clausal theories. In LICS-2001 Workshop on Logic and Learning, Boston, IEEE.

Intille, S. and Bobick, A. (1999). A framework for recognizing multi-agent action from visual evidence. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 518–525, Orlando, Florida. AAAI Press.

Isard, M. and Blake, A. (1996). Contour tracking by stochastic propagation of conditional density. In Proceedings of Fourth European conference on Computer Vision, pp. 343–356, Cambridge, UK. Springer-Verlag.

Jaakkola, T. and Jordan, M. I. (1996). Computing upper and lower bounds on likelihoods in intractable networks. In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference, pp. 340–348. Morgan Kaufmann, Portland, Oregon.

Jaakkola, T., Singh, S. P., and Jordan, M. I. (1995). Reinforcement learning algorithm for partially observable Markov decision problems. In Tesauro, G., Touretzky, D., and Leen, T. (Eds.), Advances in Neural Information Processing Systems 7, pp. 345–352, Cambridge, Massachusets. MIT Press.

Jaffar, J. and Lassez, J.-L. (1987). Constraint logic programming. In Proceedings of the Fourteenth ACM Conference on Principles of Programming Languages, pp. 111–119, Munich. Association for Computing Machinery.

Jaffar, J., Michaylov, S., Stuckey, P. J., and Yap, R. H. C. (1992a). The CLP(R) language and system. ACM Transactions on Programming Languages and Systems, 14(3), 339–395.

Jaffar, J., Stuckey, P. J., Michaylov, S., and Yap, R. H. C. (1992b). An abstract machine for CLP(R). SIGPLAN Notices, 27(7), 128–139.

Jáskowski, S. (1934). On the rules of suppositions in formal logic. Studia Logica, 1.

Jefferson, G. (1949). The mind of mechanical man: The Lister Oration delivered at the Royal College of Surgeons in England. British Medical Journal, 1(25), 1105–1121.

Jeffrey, R. C. (1983). The Logic of Decision (2nd edition). University of Chicago Press, Chicago.

Jeffreys, H. (1948). Theory of Probability. Oxford, Oxford, UK.

Jelinek, F. (1969). Fast sequential decoding algorithm using a stack. IBM Journal of Research and Development, 64, 532–556.

Jelinek, F. (1976). Continuous speech recognition by statistical methods. Proceedings of the IEEE, 64(4), 532–556.

Jelinek, F. (1997). Statistical methods for speech recognition. MIT Press, Cambridge, Massachusetts.

Jelinek, F. and Mercer, R. L. (1980). Interpolated estimation of Markov source parameters from sparse data. In Proceedings of the Workshop on Pattern Recognition in Practice, pp. 381–397, Amsterdam, London, New York. North Holland.

Jennings, H. S. (1906). Behavior of the lower organisms. Columbia University Press, New York.

Jensen, F. V. (2001). Bayesian Networks and Decision Graphs. Springer-Verlag, Berlin.

Jespersen, O. (1965). Essentials of English Grammar. University of Alabama Press, Tuscaloosa, Alabama.

Jevons, W. S. (1874). The Principles of Science. Routledge/Thoemmes Press, London.

Jimenez, P. and Torras, C. (2000). An efficient algorithm for searching implicit AND/OR graphs with cycles. Artificial Intelligence, 124(1), 1–30.

Joachims, T. (2001). A statistical learning model of text classification with support vector machines. In Proceedings of the 24th Conference on Research and Development in Information Retrieval (SIGIR), pp. 128–136, New Orleans. Association for Computing Machinery.

Johnson, W. W. and Story, W. E. (1879). Notes On the „15” puzzle. American Journal of Mathematics, 2, 397–404.

Johnson-Laird, P. N. (1988). The Computer and the Mind: An Introduction to Cognitive Science. Harvard University Press, Cambridge, Massachusetts.

Johnston, M. D. and Adorf, H.-M. (1992). Scheduling with neural networks: The case of the Hubble space telescope. Computers & Operations Research, 19(3–4), 209–240.

Jones, N. D., Gomard, C. K., and Sestoft, P. (1993). Partial Evaluation and Automatic Program Generation. Prentice-Hall, Upper Saddle River, New Jersey.

Jones, R., Laird, J. E., and Nielsen, P. E. (1998). Automated intelligent pilots for combat flight simulation. In Preceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 1047–54, Madison, Wisconsin. AAAI Press.

Jonsson, A., Morris, P., Muscettola, N., Rajan, K., and Smith, B. (2000). Planning in interplanetary space: Theory and practice. In Proceedings of the 5th International Conference on Artificial Intelligence Planning Systems (AIPS-00), pp. 177–186, Breckenridge, Colorado. AAAI Press.

Jordan, M. I. (1995). Why the logistic funciton? a tutorial discussion on probabilities and neural networks. Computational cognitive science technical report 9503, Massachusetts Institute of Technology.

Jordan, M. I. (2003). An Introduction to Graphical Models. In press.

Jordan, M. I., Ghahramani, Z., Jaakkola, T., and Saul, L. K. (1998). An introduction to variational methods for graphical models. In Jordan, M. I. (Ed.), Learning in Graphical Models. Kluwer, Dordrecht, Netherlands.

Jordan, M. I., Ghahramani, Z., Jaakkola, T., and Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2–3), 183–233.

Joshi, A. K. (1985). Tree-adjoining grammars: How much context sensitivity is required to provide reasonable structural descriptions. In Dowty, D., Karttunen, L., and Zwicky, A. (Eds.), Natural Language Parsing. Cambridge University Press, Cambridge, UK.

Joshi, A. K., Webber, B. L., and Sag, I. (1981). Elements of Discourse Understanding. Cambridge University Press, Cambridge, UK.

Joslin, D. and Pollack, M. E. (1994). Least-cost flaw repair: A plan refinement strategy for partial-order planning. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), p. 1506, Seattle. AAAI Press.

Jouannaud, J.-P. and Kirchner, C. (1991). Solving equations in abstract algebras: A rule-based survey of unification. In Lassez, J.-L. and Plotkin, G. (Eds.), Computational Logic, pp. 257–321. MIT Press, Cambridge, Massachusetts.

Judd, J. S. (1990). Neural Network Design and the Complexity of Learning. MIT Press, Cambridge, Massachusetts.

Juels, A. and Wattenberg, M. (1996). Stochastic hill-climbing as a baseline method for evaluating genetic algorithms. In Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E. (Eds.), Advances in Neural Information Processing Systems, Vol. 8, pp. 430–6. MIT Press, Cambridge, Massachusetts.

Julesz, B. (1971). Foundations of Cyclopean Perception. University of Chicago Press, Chicago.

Jurafsky, D. and Martin, J. H. (2000). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice-Hall, Upper Saddle River, New Jersey.

Kadane, J. B. and Larkey, P. D. (1982). Subjective probability and the theory of games. Management Science, 28(2), 113–120.

Kaelbling, L. P., Littman, M. L., and Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101, 99–134.

Kaelbling, L. P., Littman, M. L., and Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.

Kaelbling, L. P. and Rosenschein, S. J. (1990). Action and planning in embedded agents. Robotics and Autonomous Systems, 6(1–2), 35–48.

Kager, R. (1999). Optimality Theory. Cambridge University Press, Cambridge, UK.

Kahneman, D., Slovic, P., and Tversky, A. (Eds.). (1982). Judgment under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge, UK.

Kaindl, H. and Khorsand, A. (1994). Memory-bounded bidirectional search. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 1359–1364, Seattle. AAAI Press.

Kalman, R. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–46.

Kambhampati, S. (1994). Exploiting causal structure to control retrieval and refitting during plan reuse. Computational Intelligence, 10, 213–244.

Kambhampati, S., Mali, A. D., and Srivastava, B. (1998). Hybrid planning for partially hierarchical domains. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 882–888, Madison, Wisconsin. AAAI Press.

Kanal, L. N. and Kumar, V. (1988). Search in Artificial Intelligence. Springer-Verlag, Berlin.

Kanal, L. N. and Lemmer, J. f. (Eds.) (1986). Uncertainty in Artificial Intelligence. Elsevier/North-Holland, Amsterdam, London, New York.

Kanazawa, K., Koller, D., and Russell, S. J. (1995). Stochastic simulation algorithms for dynamic probabilistic networks. In Uncertainty in Artificial Intelligence: proceedings of the Eleventh Conference, pp. 346–351, Montreal, Canada. Morgan Kaufmann.

Kaplan, D. and Montague, R. (1960). A paradox regained. Notre Dame Journal of Formal Logic, 1(3), 79–90.

Karmarkar, N. (1984). A new polynomial-time algorithm for linear programming. Combinatorica, 4, 373–395.

Karp, R. M. (1972). Reducibility among combinatorial problems. In Miller, R. E. and Thatcher, J. W. (Eds.), Complexity of Computer Computations, pp. 85–103. Plenum, New York.

Kasami, T. (1965). An efficient recognition and syntax analysis algorithm for context-free languages. Tech. rep. AFCRL-65–758, Air Force Cambridge Research Laboratory, Bedford, Massachusetts.

Kasparov, G. (1997). IBM owes me a rematch. Time, 149(21), 66–67.

Kasper, R. T. (1988). Systemic grammar and functional unification grammar. In Benson, J. and Greaves, W. (Eds.), Systemic Functional Approaches to Discourse. Ablex, Norwood, New Jersey.

Kaufmann, M., Manolios, P., and Moore, J. S. (2000). Computer-Aided Reasoning: An Approach. Kluwer, Dordrecht, Netherlands.

Kautz, H. McAllester, D. A., and Selman, B. (1996). Encoding plans in propositional logic. In Proceedings of the Fifth International Conference on Principles of Knowledge Representation and Reasoning, pp. 374–384, Cambridge, Massachusetts. Morgan Kaufmann.

Kautz, H. and Selman, b. (1992). Planning as satisfiability. In ECAI 92: 10th European Conference on Artificial Intelligence Proceedings, pp. 359–363, Vienna. Wiley.

Kautz, H. and Selman, B. (1998). BLACKBOX: A new aproach to the application of theorem proving to problem solving. Working Notes of the AIPS-98 Workshop on Planning as Combinatorial Search.

Kavraki, L., Svestka, P., Latombe, J.-C., and Overmars, M. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.

Kay, M., Gawron, J. M., and Norvig, P. (1994). Verbmobil: A Translation System for Face-ToFace Dialog. CSLI Press, Stanford, California.

Kaye, r. (2000). Minesweeper is NP-complete!. Mathematical Intelligencer, 5(22), 9–15.

Kearns, M. (1990). The Computational Complexity of Machine Learning. MIT Press, Cambridge, Massachusetts.

Kearns, M., Mansour, Y., and Ng, A. Y. (2000). Approximate planning in large POMDPs via reusable trajectories. In Solla, S. A., Leen, T. K., and Müller, K.-R. (Eds.), Advances in Neural Information Processing Systems 12. MIT Press, Cambridge, Massachusetts.

Kearns, M. and Singh, S. P. (1998). Near-optimal reinforcement learning in polynomial time. In Proceedings of the Fifteenth International Conference on Machine Learning, pp. 260–268, Madison, Wisconsin. Morgan Kaufmann.

Kearns, M. and Vazirani, U. (1994). An Introduction to Computational Learning Theory. MIT Press, Cambridge, Massachusetts.

Keeney, R. L. (1974). Multiplicative utility functions. Operations Research, 22, 22–34.

Keeney, R. L. and Raiffa, H. (1976). Decisions with Multiple Objectives: preferences and Value Tradeoffs. Wiley, New York.

Kehler, A. (1997). Probabilistic coreference in information extraction. In Cardie, C. and Weischedel, R. (Eds.), Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, pp. 163–173. Association for Computational Linguistics, Somerset, New Jersey.

Kemp, M. (Ed.). (1989). Leonardo on Painting: An Anthology of Writings. Yale University Press, New Haven, Connecticut.

Kern, C. and Greenstreet, M. R. (1999). Formal verification in hardware design: A survey. ACM Transactions on Design Automation of Electronic Systems, 4(2), 123–193.

Keynes, J. M. (1921). A Treatise on Probability. Macmillan, London.

Khatib, O. (1986). Real-time obstacle avoidance for robot manipulator and mobile robots. The International Journal of Robotics Research, 5(1), 90–98.

Kietz, J.-U. and Dzeroski, S. (1994). Inductive logic programming and learnability. SIGART Bulletin, 5(1), 22–32.

Kim, J. H. (1983). CONVINCE: A Conversational Inference Consolidation Engine. Ph.D. thesis, Department of Computer Science, University of California at Los Angeles.

Kim, J. H. and Pearl, J. (1983). A computational model for combined causal and diagnostic reasoning in inference systems. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83), pp. 190–193, Karlsruhe, Germany. Morgan Kaufmann.

Kim, J. H. and Pearl, J. (1987). CONVINCE: A conversational inference consolidation engine. IEEE Transactions on Systems, Man, and Cybernetics, 17(2), 120–132.

King, R. D., Muggleton, S. H., Lewis, R. A., and Sternberg, M. J. E. (1992). Drug design by machine learning: The use of inductive logic programming to model the structure activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences of the United States of America, 89(23), 11322–11326.

Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Sience, 220, 671–680.

Kirkpatrick, S. and Selman, b. (1994). Critical behavior in the satisfiability of random Boolean expressions. Science, 264(5163), 1297–1301.

Kirousis, L. M. and Papadimitriou, C. H. (1988). The complexity of recognizing polyhedral scenes. Journal of Computer and System Sciences, 37(1), 14–38.

Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., and Osawa, E. (1997). RoboCup: The robot world cup initiative. In Johnson, W. L. and Hayes-Roth, B. (Eds.), Proceedings of the First International Conference on Autonomous Agents, pp. 340–347, New York. ACM Press.

Kjaerulff, U. (1992). A computational scheme for reasoning in dynamic probabilistic networks. In Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference, pp. 121–129, Stanford, California. Morgan Kaufmann.

Klein, D. and Manning, C. D. (2001). Parsing with treebank grammars: Empirical bounds, theoretical models, and the structure of the Penn treebank. In Proceedings of the 39th Annual Meeting of the ACL.

Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632.

Knight, K. (1999). A statistical mt tutorial workbook. prepared in connection with the Johns Hopkins University summer workshop.

Knoblock, c. A. (1990). Learning abstraction hierarchies for problem solving. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Vol. 2, pp. 923–928, Boston. MIT Press.

Knuth, D. E. (1968). Semantics for context-free languages. Mathematical Systems Theory, 2(2), 127–145.

Knuth, D. E. (1973). The Art of Computer Programming (second edition)., Vol. 2: Fundamental Algorithms. Addison-Wesley, Reading, Massachusetts.

Knuth, D. E. (1975). An analysis of alpha–beta pruning. Artificial Intelligence, 6(4), 293–326.

Knuth, D. E. and Bendix, P. B. (1970). Simple word problems in universal algebras. In Leech, J. (Ed.), Computational Problems in Abstract Algebra, pp. 263–267. Pergamon, Oxford, UK.

Koditschek, D. (1987). Exact robot navigation by means of potential functions: some topological considerations. In Proceedings of the 1987 IEEE International Conference on Robotics and Automation, Vol. 1, pp. 1–6, Raleigh, North Carolina. IEEE Computer Society Press.

Koehler, J., Nebel, B., Hoffman, J., and Dimopoulos, y. (1997). Extending planning graphs to an ADL subset. In Proceedings of the Fourth European Conference on Planning, pp. 273–285, Toulouse, France. Springer-Verlag.

Koenderink, J. J. (1990). Solid Shape. MIT Press, Cambridge, Massachusetts.

Koenderink, J. J. and van Doorn, A. J. (1975). Invariant properties of the motion parallax field due to the movement of rigid bodies relative to an boserver. Optica Acta, 22(9), 773–791.

Koenderink, J. J. and van Doorn, A. J. (1991). Affine structure from motion. Journal of the Optical Society of America A, 8, 377–385.

Koenig, s. (1991). Optimal probabilistic and decision-theoretic planning using Markovian decision theory. Master’s report, Computer Science Division, University of California, Berkeley.

Koenig, S. (2000). Exploring unknown environments with real-time search or reinforcement learning. In solla, S. A., Leen, T. K., and Müller, K.-R. (Eds.), Advances in Neural Information Processing Systems 12. MIT Press, Cambridge, Massachusetts.

Koenig, s. and Simmons, R. (1998). Solving robot navigation problems with initial pose uncertainty using real-time heuristic search. In aips98. AAAI Press, Menlo Park, California.

Kohn, W. (1991). Declarative control architecture. Communications of the Association for Computing Machinery, 34(8), 65–79.

Koller, D., Meggido, N., and von Stengel, B. (1996). Efficient computation of equilibria for extensive two-person games. Games and Economic Behaviour, 14(2), 247–259.

Koller, D. and Pfeffer, A. (1997). Representations and solutions for game-theoretic problems. Artificial Intelligence, 94(1–2), 167–215.

Koller, d. and Pfeffer, a. (1998). Probabilistic frame-based systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 580–587, Madison, Wisconsin. AAAI Press.

Koler, d. and Sahami, M. (1997). Hierarchically classifying documents using very few words. In Proceedings of the Fourteenth International Conference on Machine Learning, pp. 170-178. Morgan Kaufmann.

Kolmogorov, A. N. (1941). Interpolation und extrapolation von stationaren zufalligen folgen. Bulletin of the Academy of Sciences of the USSR, Ser. Math. 5, 3–14.

Kolmogorov, A. N. (1950). Foundations of the Theory of Probability. Chelsea, New York.

Kolmogorov, A. N. (1963). On tables of random numbers. Sankhya, the Indian Journal of Statistics, Series A 25.

Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems in Information Transmission, 1(1), 1–7.

Kolodner, J. (1983). Reconstructive memory: A computer model. Cognitive Science, 7, 281–328.

Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann, San Mateo, California.

Kondrak, G. and van Beek, P. (1997). A theoretical evaluation of selected backtracking algorithms. Artificial Intelligence, 89, 365–387.

Konolige, K. (1997). COLBERT: A language for reactive control in Saphira. In KI-97: Advances in Artificial Intelligence, LNAI, p. 31–52. Springer-Verlag.

Konolige, K. (1982). A first order formalization of knowledge and action for a multi-agent planning system. In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood, Chichester, England.

Koopmans, T. C. (1972). Representation of preference orderings over time. In McGuire, C. B. and Radner, R. (Eds.), Decision and Organization. Elsevier/North-Holland, Amsterdam, London, New York.

Korf, R. E. (1985a). Depth-first iterative-deepening: an optimal admissible tree search. Artificial Intelligence, 27(1), 97–109.

Korf, R. E. (1985b). Iterative-deepening A*: An optimal admissible tree search. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85), pp. 1034–1036, Los Angeles. Morgan Kaufmann.

Korf, R. E. (1987). Planning as search: A quantitative approach. Artificial Intelligence, 33(1), 65–88.

Korf, R. E. (1988). Optimal path finding algorithms. In Kanal, L. N. and Kumar, V. (Eds.), Search in Artificial Intelligence, chap. 7, pp. 223–267. Springer-Verlag, Berlin.

Korf, R. E. (1990). Real-time heuristic search. Artificial Intelligence, 42(3), 189–212.

Korf, R. E. (1991). Best-first search with limited memory. UCLA Computer Science Annual.

Korf, R. E. (1993). Linear-space best-first search. Artificial Intelligence, 62(1), 41–78.

Korf, R. E. (1995). Space-efficient search algorithms. ACM Computing Surveys, 27(3), 337–339.

Korf, R. E. and Chickering, D. M. (1996). Best-first minimax search. Artificial Intelligence, 84(1–2), 299–337.

Korf, R. E. and Felner, a. (2002). Disjoint pattern database heuristics. Artificial Intelligence, 134(1–2), 9–22.

Korf, R. E. and Zhang, W. (2000). Divide-and-conquer frontier search applied to optimal sequence alignment. In Proceedings of the 17th National Conference on Artificial Intelligence, pp. 910–916, Cambridge, Massachusetts. MIT Press.

Kortenkamp, D., Bonasso, R. P., and Murphy, R. (Eds.). (1998). AI-based Mobile Robots: Case studies of successful robot systems, Cambridge, MA. MIT Press.

Kotok, a. (1962). A chess playing program for the IBM 7090. Ai project memo 41, MIT Computation Center, Cambridge, Massachusetts.

Koutsoupias, E. and Papadimitriou, C. H. (1992). On the greedy algorithm for satisfiability. Information Processing Letters, 43(1), 53–55.

Kowalski, R. (1974). Predicate logic as a programming language. In Proceedings of the IFIP-74 Congress, pp. 569–574. Elsevier/North-Holland.

Kowalski, R. (1979a). Algorithm = logic + control. Communications of the Association for Computing Machinery, 22, 424–436.

Kowalski, R. (1979b). Logic for Problem Solving. Elsevier/North-Holland, Amsterdam, London, New York.

Kowalski, R. (1988). The early years of logic programming. Communications of the Association for Computing Machinery, 31, 38–43.

Kowalski, R. and Kuehner, D. (1971). Linear resolution with selection function. Artificial Intelligence, 2(3–4), 227–260.

Kowalski, R. and Sergot, M. (1986). A logic-based calculus of events. New Generation Computing, 4(1), 67–95.

Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, Massachusetts.

Koza, J. R. (1994). Genetic Programming II: Automatic discovery of reusable programs. MIT Press, Cambridge, Massachusetts.

Koza, J. R., Bennett, F. H., Andre, D., and Keane, M. A. (1999). Genetic Programming III: Darwinian invention and problem solving. Morgan Kaufmann, San Mateo, California.

Kraus, S., Ephrati, E., and Lehmann, D. (1991). Negotiation in a non-cooperative environment. Journal of Experimental and Theoretical Artificial Intelligence, 3(4), 255–281.

Kripke, S. A. (1963). Semantical considerations on modal logic. Acta Philosophica Fennica, 16, 83–94.

Krovetz, R. (1993). Viewing morphology as an inference process. In Proceedings of the Sixteenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, pp. 191–202, New York. ACM Press.

Kruppa, E. (1913). Zur Ermittlkung eines Objecktes aus zwei Perspektiven mit innerer Orientierung. Sitz.-Ber. Akad. Wiss., Wien, Math. Naturw., Kl. Abt. Ila, 122, 1939–1948.

Kuhn, H. W. (1953). Extensive games and the problem of information. In Kuhn, H. W. and Tucker, A. W. (Eds.), Contributions to the Theory of Games II. Princeton University Press, Princeton, New Jersey.

Kuipers, B. J. and Levitt, T. S. (1988). Navigation and mapping in large-scale space. AI Magazine, 9(2), 25–43.

Kukich, K. (1992). Techniques for automatically correcting words in text. ACM Computing Surveys, 24(4), 377–439.

Kumar, P. R. and Varaiya, P. (1986). Stochastic systems: Estimation, identification, and adaptive control. Prentice-Hall, Upper Saddle River, New Jersey.

Kumar, V. (1992). Algortihms for constraint satisfaction problems: A survey. AI Magazine, 13(1), 32–44.

Kumar, V. and Kanal, L. N. (1983). A general branch and bound formulation for understanding and synthesizing and/or tree search procedures. Artificial Intelligence, 21, 179–198.

Kumar, V. and Kanal, L. N. (1988). The CDP: A unifying formulation for heuristic search, dynamic programming, and branch-and-bound. In Kanal, L. N. and Kumar, V. (Eds.), Search in Artificial Intelligence, chap. 1, pp. 1–27. Springer-Verlag, Berlin.

Kumar, V., Nau, D. S., and Kanal, L. N. (1988). A general branch-and bound formulation for AND/OR grap and game tree search. In Kanal, L. N. and Kumar, V. (Eds.), Search in Artificial Intelligence, chap. 3, pp. 91–130. Springer-Verlag, Berlin.

Kuper, g. M. and Vardi, M. Y. (1993). On the complexity of queries in the logical data model. Theoretical Computer Science, 116(1), 33–57.

Kurzweil, R. (1990). The Age of Intelligent Machines. MIT Press, Cambridge, Massachusetts.

Kurzweil, r. (2000). The Age of Spiritual Machines. Penguin.

Kyburg, H. E. (1977). Randomness and the right reference class. The Journal of Philosophy, 74(9), 501–521.

Kyburg, H. E. (1983). The reference class. Philosophy of Science, 50, 374–397.

La Mettrie, J. O. (1748). L’homme machine. E. Luzac, Leyde, France.

La Mura, P. and Shoham, Y. (1999). Expected utility networks. In Uncertainty in Artificial Intelligence: Proceedings of the Fifteenth Conference, pp. 366–373, Stockholm. Morgan Kaufmann.

Ladkin, P. (1986a). Primitives and units for time specification. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), Vol. 1, pp. 354–359, Philadelphia. Morgan Kaufmann.

Ladkin, P. (1986b). Time representation: a taxonomy of interval relations. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), Vol. 1, pp. 360–366, Philadelphia. Morgan Kaufmann.

Lafferty, J. and Zhai, C. (2001). Probabilistic relevance models based on document and query generation. In Proceedings of the Workshop on Language Modelling and Information retrieval.

Laird, J. E., Newell, A., and Rosenbloom, P. S. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, 33(1), 1–64.

Laird, J. E., Rosenbloom, P. S., and Newell, A. (1986). Chunking in Soar: The anatomy of a general learning mechanism. Machine Learning, 1, 11–46.

Lakoff, G. (1987). Women, Fire, and Dangerous Things: What Categories Reveal about the Mind. University of Chicago Press, Chicago.

Lakoff, G. and Johnson, M. (1980). Metaphors We Live By. University of Chicago Press, Chicago.

Lamarck, J. B. (1809). Philosophie zoologique. Chez Dentu et L’Auteur, Paris.

Langley, P., Simon, H. A., Bradshaw, G. L., and Zytkow, J. M. (1987). Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press, Cambridge, Massachusetts.

Langton, C. (Ed.). (1995). Artificial life. MIT Press, Cambridge, Massachusetts.

Laplace, P. (1816). Essai philosophique sur les probabilités (3rd edition). Courcier Imprimeur, Paris.

Lappin, S. and Leass, H. J. (1994). An algorithm for pronominal anaphora resolution. Computational Linguistics, 20(4), 535–561.

Lari, K. and Young, S. J. (1990). The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer, Speech and Language, 4, 35–56.

Larrañaga, P., Kuijpers, C., Murga, R., Inza, I., and Dizdarevic, S. (1999). Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review, 13, 129–170.

Latombe, J.-C. (1991). Robot Motion Planning. Kluwer, Dordrecht, Netherlands.

Lauritzen, S. (1995). The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis, 19, 191–201.

Lauritzen, S. (1996). Graphical models. Oxford University Press, Oxford, UK.

Lauritzen, S., Dawid, A., Larsen, B., and Leimer, H. (1990). Independence properties of directed Markov fields. Networks, 20(5), 491–505.

Lauritzen, S. and Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, B 50(2), 157–224.

Lauritzen, S. and Wermuth, N. (1989). Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17, 31–57.

Lavrac, N. and Dzeroski, S. (1994). Inductive Logic Programming: Techniques and Applications. Ellies Horwood, Chichester, England.

Lawler, E. L. (1985). The travelling salesman problem: A guided tour of combinatorial optimization. Wiley, New York.

Lawler, E. L., Lenstra, J. K., Kan, A., and Shmoys, D. B. (1992). The Travelling Salesman Problem. Wiley Interscience.

Lawler, E. L., Lenstra, J. K., Kan, A., and Shmoys, D. B. (1993). Sequencing and scheduling: algorithms and complexity. In Graves, S. c., Zipkin, P. H., and Kan, A. h. g. r. (Eds.), Logistics of Production and Inventory: Handbooks in Operations Research and Management Science, Volume 4, pp. 445–522. North-Holland, Amsterdam.

Lawler, E. L. and Wood, D. E. (1966). Branch-and-bound methods: A survey. Operations Research, 14(4), 699–719.

Lazanas, A. and Latombe, J.-C. (1992). Landmark-based robot navigaton. In Preceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 816–822, San Jose. AAAI Press.

Le Cun, Y., Jackel, L., Boser, B., and Denker, J. (1989). Handwritten digit recognition: Applications of neural network chips and automatic learning. IEEE Communications Magazine, 27(11), 41–46.

LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P., and Vapnik, V. N. (1995). Comparison of learning algorithms for handwritten digit recognition. In Fogelman, F. and Gallinari, P. (Eds.), International Conference on Artificial Neural Networks, pp. 53–60, Berlin. Springer-Verlag.

Leech, G., Rayson, P., and Wilson, A. (2001). Word Frequencies in Written and Spoken English: Based on the British National Corpus. Longman, New York.

Lefkovitz, D. (1960). A strategic pattern recognition program for the game Go. Technical note 60–243, Wright Air Development Division, University of Pennsylvania, Moore School of Electrical Engineering.

Lenat, D. B. (1983). EURISKO: A program that learns new heuristics and domain concepts: The nature of heuristics, III: Program design and results. Artificial Intelligence, 21(1–2), 61–98.

Lenat, D. B. (1995). Cyc: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11).

Leant, D. B. and Brown, J. S. (1984). Why AM and EURISKO appear to work. Artificial Intelligence, 23(3), 269–294.

Leant, D. B. and Guha, R. V. (1990). Building Large Knowledge-Based Systems: Representation and Inference in the CYC project. Addison-Wesley, Reading, Massachusetts.

Leonard, H. S. and Goodman, N. (1940). The calculus of individuals and its uses. Journal of Symbolic Logic, 5(2), 45–55.

Leonard, J. J. and Durrant-Whyte, H. (1992). Directed sonar sensing for mobile robot navigation. Kluwer, Dordrecht, Netherlands.

Les′niewski, S. (1916). Podstawy ogólnej teorii mnogos′ci. Moscow.

Lettvin, J. Y., Maturana, H. R., McCulloch, W. S., and Pitts, W. H. (1959). What the frog’s eye tells the frog’s brain. Proceedings of the IRE, 47(11), 1940–1951.

Letz, r., Schumann, J., Bayerl, S., and Bibel, W. (1992). SETHEO: A high-performance theorem prover. Journal of Automated Reasoning, 8(2), 183–212.

Levesque, H. J. and Brachman, R. J. (1987). Expressiveness and tractability in knowledge representation and reasoning. Computational Intelligence, 3(2), 78–93.

Levesque, H. J., Reiter, R., Lespérance, Y., Lin, F., and Scherl, R. (1997a). GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming, 31, 59–84.

Levesque, H. J., Reiter, R., Lespérance, y., Lin, F., and Scherl, R. (1997b). GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming, 31, 59–84.

Levitt, G. M. (2000). The Turk, Chess Automation. McFarland and Company.

Levy, D. N. L. (Ed.). (1988a). Computer Chess Compendium. Springer-Verlag, Berlin.

Levy, d. N. L. (Ed.). (1988b). Computer Games. Springer-Verlag, Berlin.

Lewis, D. D. (1998). Naive Bayes at forty: The independence assumption in information retrieval. In Machine Learning: ECML-98. 10th European Conference on Machine Learning. Proceedings, pp. 4–15, Chemnitz, Germany. Springer-Verlag.

Lewis, D. K. (1966). An argument for the identity theory. the Journal of Philosophy, 63(1), 17–25.

Lewis, D. K. (1972). General semantics. In Davidson, d. and Harman, G. (Eds.), Semantics of Natural Language, pp. 169–218. Reidel, Dordrecht, Netherlands.

Lewis, D. K. (1980). Mad pain and Martian pain. In Block, N. (Ed.), Readings in Philosophy of Psychology, Vol. 1, pp. 216–222. Harvard University Press, Cambridge, Massachusetts.

Li, C. M. and Anbulagan (1997). Heuristics based on unit propagation for satisfiability problems. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 366–371, Nagoya, Japan. Morgan Kaufmann.

Li, M. and Vitanyi, P. M. B. (1993). An Introduction to Kolmogorov Complexity and Its Applications. Springer-Verlag, Berlin.

Lifschitz, V. (1986). On the semantics of STRIPS. In Georgeff, M. P. and Lansky, a. L. (Eds.), Reasoning about Actions and Plans: Proceedings of the 1986 Workshop, pp. 1–9, timberline, Oregon. Morgan Kaufmann.

Lifschitz, V. (2001). Answer set programming and plan generation. Artificial Intelligence, 138(1–2), 39–54.

Lighthill, J. (1973). Artificial intelligence: A general survey. In Lighthill, J., Sutherland, N. s., Needham, r. M., Longuet-Higgins, H. c., and Michie, D. (Eds.), Artificial Intelligence: A Paper Symposium. Science Research Council of Great Britain, London.

Lin, F. and Reiter, R. (1997). How to progress a database. Artificial Intelligence, 92(1–2), 131–167.

Lin, S. (1965). Computer solutions of the travelling salesman problem. Bell Systems Technical Journal, 44(10), 2245–2269.

Lin, S. and Kernighan, b. W. (1973). An effective heuristic algorithm for the travelling-salesman problem. Operations Research, 21(2), 498–516.

Linden, T. A. (1991). Representing software design as partially developed plans. In Lowry, M. R. and McCartney, R. D. (Eds.), Automating Software Design, pp. 603–625. MIT Press, Cambridge, Massachusetts.

Lindsay, r. K. (1963). Inferential memory as the basis of machines which understand natural language. In Feigenbaum, E. A. and Feldman, J. (Eds.), Computers and Thought, pp. 217–236. McGraw-Hill, New Jork.

Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., and Lederberg, J. (1980). Applications of Artificial Intelligence for Organic Chemistry: The DENDRAL Project. McGraw-Hill, New York.

Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the 11th International Conference on Machine Learning (ML-94), pp. 157–163, Mew Brunswick, NJ. Morgan Kaufmann.

Littman, M. L., Keim, g. A., and Shazeer, N. M. (1999). Solving crosswords with PROVERB. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 914–915, Orlando, Florida. AAAI Press.

Liu, J. S. and Chen, R. (1998). Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 93, 1022–1031.

Lloyd, J. W. (1987). Foundations of Logic Programming. Springer-Verlag, Berlin.

Locke, J. (1690). An Essay Concerning Human Understanding. William Tegg.

Locke, W. N. and Booth, A. D. (1955). Machine Translation of Languages: Fourteen Essays. MIT Press, Cambridge, Massachusetts.

Lodge, D. (1984). Small World. Penguin Books, New York.

Lohn, J. D., Kraus, W. F., and Colombano, S. P. (2001). Evolutionary optimization of yagi-uda antennas. In Proceedings of the Fourth International Conference on Evolvable Systems, pp. 236–243.

Longuet-Higgins, H. C. (1981). A computer algorithm for reconstructing a scene from two projections. Nature, 293, 133–135.

Lovejoy, W. S. (1991). A survey of algorithmic methods for partially observed Markov decision processes. Annals of Operations Research, 28(1–4), 47–66.

Loveland, D. (1968). Mechanical theorem proving by model elimination. Journal of the Association for Computing Machinery, 15(2), 236–251.

Loveland, d. (1970). A linear format for resolution. In Proceedings of the IRIA Symposium on Automatic Demonstration, pp. 147–162, Berlin. Springer-Verlag.

Loveland, D. (1984). Automated theorem-proving: A quarter-century review. Contemporary Mathematics, 29, 1–45.

Lowe, D. g. (1987), Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence, 31, 355–395.

Löwenheim, L. (1915). Über möglichkeiten im Relativkalkül. Mathematische Annalen, 76, 447– 470.

Lowerre, b. T. (1976). The Harpy Speech Recognition System. Ph.D. thesis, Computer Science Department, Carnegie-Mellon University, Pittsburgh, Pennsylvania.

Lowerre, B. T. and Reddy, R. (1980). The harpy speech recognition system. In Lea, W. A. (Ed.), Trends in Speech Recognition, chap. 15. Prentice-Hall, Upper Saddle River, New Jersey.

Lowry, M. R. and McCartney, R. D. (1991). Automating Software Design. MIT Press, Cambridge, Massachusetts.

Loyd, D. (1959). Mathematical Puzzles of Sam Loyd: Selected and Edited by Martin Gardner. Dover, New York.

Lozano-Perez, T. (1983). Spatial planning: A configuration space approach. IEEE Transactions on Computers, C-32(2), 108–120.

Lozano-Perez, T., Mason, M., and Tylor, R. (1984). Automatic synthesis of fine-motion strategies for robots. International Journal of Robotics Research, 3(1), 3–24.

Luby, M., Sinclair, A., and Zuckerman, D. (1993). Optimal speedup of Las Vegas algorithms. Information Processing Letters, 47, 173–180.

Luby, M. and Vigoda, E. (1999). Fast convergence of the glauber dynamics for sampling independent sets. Random Structures and Algorithms, 15(3–4), 229–241.

Lucas, J. R. (1961). Minds, machines, and Gödel. Philosophy, 36.

Lucas, J. R. (1976). This Gödel is killing me: A rejoinder. Philosophia, 6(1), 145–148.

Lucas, P. (1996). Knowledge acquisition for decision-theoretic expert systems. AISB Quarterly, 94, 23–33.

Luce, D. R. and Raiffa, H. (1957). Games and Decisions. Wiley, New York.

Luger, G. F. (Ed.). (1995). Computation and intelligence: Collected readings. AAAI Press, Menlo Park, California.

MacKay, D. J. C. (1992). a practical Bayesian framework for back-propagation networks. Neural Computation, 4(3), 448–472.

Mackworth, A. K. (1973). Interpreting pictures of polyhedral scenes. Artificial Intelligence, 4, 121–137.

Mackworth, A. K. (1977). Consistency in networks of relations. Artificial Intelligence, 8(1), 99–118.

Mackworth, A. K. (1992). Constraint satisfaction. In Shapiro, S. (Ed.), Encyclopedia of Artificial Intelligence (second edition)., Vol. 1, pp. 285–293. Wiley, New York.

Mahanti, A. and Daniels, C. J. (1993). A SIMD approach to parallel heuristic search. Artificial Intelligence, 60(2), 243–282.

Majercik, S. M. and Littman, M. L. (1999). Planning under uncertainty via stochastic satisfiability. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, pp. 549–556.

Malik, J, (1987). Interpreting line drawings of curved objects. International Journal of Computer Vision, 1(1), 73–103.

Malik, J. and Rosenholtz, r. (1994). Recovering surface curvature and orientation from texture distortion: A least squares algorithm and sensitivity analysis. In Eklundh, J.-O. (Ed.), Proceedings of the Third European Conf. on Computer Vision, pp. 353–364, Stockholm. Springer-Verlag.

Malik, J. and Rosenholtz, R. (1997). Computing local surface orientation and shape from texture for curved surfaces. International Journal of Computer Vision, 23(2), 149–168.

Mann, W. C. and Thompson, S. A. (1983). Relational propositions in discourse. Tech. rep. RR-83-115, Information Sciences Institute.

Mann, W. C. and Thompson, S. A. (1988). Rhetorical structure theory: Toward a functional theory of text organization. Text, 8(3), 243–281.

Manna, Z. and Waldinger, R. (1971). Toward automatic program synthesis. Communications of the Association for Computing Machinery, 14(3), 151–165.

Manna, Z. and Waldinger, R. (1985). The Logical Basis for Computer Programming: Volume 1: Deductive Reasoning. Addison-Wesley, Reading, Massachusets.

Manna, Z. and Waldinger, R. (1986). Special relations in automated deduction. Journal of the Association for Computing Machinery, 33(1), 1–59.

Manna, Z. and Waldinger, R. (1992). Fundamentals of deductive program synthesis. IEEE Transactions on Software Engineering, 18(8), 674– 704.

Manning, C. D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.

Marbach, P. and Tsitsiklis, J. N. (1998). Simulation-based optimization of Markov reward processes. Technical report LIDS-P-2411, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology.

Marcus, M. P., Santorini, B., and Marcinkiewicz, M. A. (1993). Building a large annotated corpus of english: The penn treebank. Computational Linguistics, 19(2), 313–330.

Markov, A. A. (1913). An example of statistical investigation in the text of „Eugene Onegin” illustrating coupling of „tests” in chains. Proceedings of the Academy of Sciences of St. Petersburg, 7.

Maron, M. E. (1961). Automatic indexing: An experimental inquiry. Journal of the Association for Computing Machinery, 8(3), 404–417.

Maron, M. E. and Kuhns, J.-L. (1960). On relevance, probabilistic indexing and information retrieval. Communications of the ACM, 7, 219–244.

Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman, New York.

Marriott, K. and Stuckey, P. J. (1998). Programming with Constraints: An Introduction. MIT Press, Cambridge, Massachusetts.

Marsland, a. T. and Schaeffer, J. (Eds.). (1990). Computers, Chess, and Cognition. Springer-Verlag, Berlin.

Martelli, A. and Montanari, U. (1976). Unification in linear time and space: A structured presentation. Internal report B 76-16, Istituto di Elaborazione della Informazione, Pisa, Italy.

Martelli, A. and Montanari, U. (1978). Optimizing decision trees through heuristically guided search. Communications of the Association for Computing Machinery, 21, 1025–1039.

Marthi, B., Pasula, H., Russell, S. J., and Peres, Y. (2002). Decayed MCMC filtering. In Uncertainty in Artificial Intelligence: Proceedings of the Eighteenth Conference, pp. 319–326, Edmonton, Alberta. Morgan Kaufmann.

Martin, J. H. (1990). A Computational Model of Metaphor Interpretation. Academic Press, New York.

Martin, P. and Shmoys, D. B. (1996). A new approach to computing optimal schedules for the jobshop scheduling problem. In Proceedings of the 5th International IPCO Conference, pp. 389–403. Springer-Verlag.

Maslov, S. Y. (1964). An inverse method for establishing deducibility in classical predicate calculus. Doklady Akademii nauk SSSR, 159, 17–20.

Maslov, S. Y. (1967). An inverse method for establishing deducibility of nonprenex formulas of the predicate calculus. Doklady Akademii nauk SSSR, 172, 22–25.

Mason, M. (1993). Kicking the sensing habit. AI Magazine, 14(1), 58–59.

Mason, M. (2001). Mechanics of Robotic Manipulation. MIT Press.

Mason, M. and Salisbury, J. (1985). Robot hands and the mechanics of manipulation. MIT Press.

Mataric, M. J. (1997). Reinforcement learning in the multi-robot domain. Autonomous Robots, 4(1), 73–83.

Mates, B. (1953). Stoic Logic. University of California Press, Berkeley and Los Angeles.

Maxwell, J. and Kaplan, r. (1993). The interface between phrasal and functional constraints. Computational Linguistics, 19(4), 571–590.

Maxwell, J. and Kaplan, R. (1995). A method for disjunctive constraint satisfaction. In Dalrymple, M., Kaplan, r., Maxwell, J., and Zaenen, A. (Eds.), Formal Issues in Lexical-Functional Grammar, No. 47 in CSLI Lecture Note Series, chap. 14, pp. 381–481. CSLI Publications.

McAllester, D. A. (1980). An outlook on truth maintenance. Ai memo 551, MIT AI Laboratory, Cambridge, Massachusetts.

McAllester, D. A. (1988). Conspiracy numbers for min-max search. Artificial Intelligence, 35(3), 287–310.

McAllester, D. A. (1989). Ontic: A Knowledge Representation System for Mathematics. MIT Press, Cambridge, Massachusetts.

McAllester, D. A. (1998). What is the most pressing issue facing ai and the aaai today?. Candidate statement, election for Councilor of the American Association for Artificial Intelligence.

McAllester, D. A. and Givan, R. (1992). Natural language syntax and first-order inference. Artificial Intelligence, 56(1), 1–20.

McAllester, D. A. and Rosenblitt, D. (1991). Systematic nonlinear planning. In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Vol. 2, pp. 634–639, Anaheim, California. AAAI Press.

McCarthy, J. (1958). Programs with common sense. In Proceedings of the Symposium on Mechanisation of Thought Processes, Vol. 1, pp. 77–84, London. Her Majesty’s Stationery Office.

McCarthy, J. (1963). Situations, actions, and causal laws. Memo 2, Stanford University Artificial Intelligence Project, Stanford, California.

McCarthy, J. (1968). Programs with common sense. In Minsky, M. L. (Ed.), Semantic Information Processing, pp. 403–418. MIT Press, Cambridge, Massachusetts.

McCarthy, J. (1980). Circumscription: A form of non-monotonic reasoning. Artificial Intelligence, 13(1–2), 27–39.

McCarthy, J. and Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 463–502. Edinburgh University Press, Edinburgh, Scotland.

McCarthy, J., Minsky, M. L., Rochester, N., and Shannon, c. E. (1955). Proposal for the Dartmouth summer research project on artificial intelligence. Tech. rep., Dartmouth College.

McCawley, J. D. (1988). The Syntactic Phenomena of English, Vol. 2 volumes. University of Chicago Press.

McCawley, J. D. (1993). Everything That Linguists Have Always Wanted to Know about Logic but Were Ashamed to Ask (Second edition). University of Chicago Press, Chicago.

McCulloch, W. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–137.

McCune, W. (1992). Automated discovery of new axiomatizations of the left group and right group calculi. Journal of Automated Reasoning, 9(1), 1–24.

McCune, W. (1997). Solution of the robbins problem. Journal of Automated Reasoning, 19(3), 263–276.

McDermott, D. (1976). Artificial intelligence meets natural stupidity. SIGART Newsletter, 57, 4–9.

McDermott, D. (1978a). Planning and acting. Cognitive Science, 2(2), 71–109.

McDermott, D. (1978b). Tarskian semantics, or, no notation without denotation!. Cognitive Science, 2(3).

McDermott, D. (1987). A critique of pure reason. Computational Intelligence, 3(3), 151–237.

McDermott, D. (1996). A heuristic estimator for means-ends analysis in planning. In Proceedings of the Third International Conference on AI Planning Systems, pp. 142–149, Edinburgh, Scotland. AAAI Press.

McDermott, D. and Doyle, J. (1980). Non-monotonic logic: i. Artificial Intelligence, 13(1–2), 41–72.

McDermott, J. (1982). R1: A rule-based configurer of computer systems. Artificial Intelligence, 19(1), 39–88.

McEliece, R. J., MacKay, D. J. C., and Cheng, J.-F. (1998). Turbo decoding as an instance of Pearl’s „belief propagation” algorithm. IEEE Journal on Selected Areas in Communications, 16(2), 140– 152.

McGregor, J. J. (1979). Relational consistency algorithms and their application in finding subgraph and graph isomorphisms. Information Sciences, 19(3), 229–250.

McKeown, K. (1985). Text Generation: Using Discourse Strategies and Focus Constraints to Generate Natural Language Text. Cambridge University Press, Cambridge, UK.

McLachlan, g. J. and Krishnan, t. (1997). The EM Algorithm and Extensions. Wiley, New York.

McMillan, K. L. (1993). Symbolic Model Checking. Kluwer, Dordrecht, Netherlands.

Meehl, P. (1955). Clinical vs. Statistical Prediction. University of Minnesota Press, Minneapolis.

Melc′uk, I. A. and Polguere, A. (1988). A formal lexicon in the meaning-text theory (or how to do lexica with words). Computational Linguistics, 13(3–4), 261–275.

Mendel, G. (1866). Versuche über pflanzen-hybriden. Verhandlungen des Naturforschenden Vereins, Abhandlungen, Brünn, 4, 3–47. Translated into English by C. T. Druery, published by Bateson (1902).

Mercer, J. (1909). Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Roy. Soc. London, A, 209, 415–446.

Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1091.

Mézard, M. and Nadal, J.-P. (1989). Learning in feed-forward layered networks: The tiling algorithm. Journal of Physics, 22, 2191–2204.

Michalski, R. S. (1969). On the quasi-minimal solution of the general covering problem. In Proceedings of the First International Symposium on Information Processing, pp. 125–128.

Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (Eds.). (1983). Machine Learning: An Artificial Intelligence Approach, Vol. 1. Morgan Kaufmann, San Mateo, California.

Michalski, r. S., Carbonell, J. G., and Mitchell, T. M. (Eds.). (1986a). Machine Learning: An Artificial Intelligence Approach, Vol. 2. Morgan Kaufmann, San Mateo, California.

Michalski, r. S., Mozetic, I., Hong, J., and Lavrac, N. (1986b). The multi-purpose incremental learning system aq15 and its testing application to three medical domains. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), pp. 1041–1045, Philadelphia. Morgan Kaufmann.

Michel, S. and Plamondon, P. (1996). Bilingual sentence alignment: Balancing robustnes and accuracy. In Proceedings of the Conference of the Association for Machine Translation in the Americas (AMTA).

Michie, d. (1966). Game-playing and game-learning automata. In Fox, L. (Ed.), Advances in Programming and Non-Numerical Computation, pp. 183– 200. Pergamon, Oxford, UK.

Michie, D. (1972). Machine intelligence at Edinburgh. Management Informatics, 2(1), 7–12.

Michie, D. (1974). Machine intelligence at Edinburgh. In On Intelligence, pp. 143–155. Edinburgh University Press.

Michie, d. and Chambers, r. A. (1968). BOXES: An experiment in adaptive control. In Dale, E. and Michie, D. (Eds.), Machine Intelligence 2, pp. 125–133. Elsevier/North-Holland, Amsterdam, London, New York.

Michie, d., Spiegelhalter, D. J., and Taylor, C. (Eds.). (1994). Machine Learning, Neural and Statistical Classification. Ellis Horwood, Chichester, England.

Milgrom, P. (1997). Putting auction theory to work: The simultaneous ascending auction. Tech. rep. Technical Report 98–0002, Stanford University Department of Economics.

Mill, J. S. (1843). A System of Logic, Ratiocinative and Inductive: Being a Connected View of the Principles of Evidence, and Methods of Scientific Investigation. J. W. Parker, London.

Mill, J. S. (1863). Utilitarianism. Parker, Son and Bourn, London.

Miller, A. C., Merkhofer, M. M., Howard, R. A., Matheson, J. E., and Rice, T. R. (1976). Development of automated aids for decision analysis. Technical report, SRI International, Menlo Park, California.

Minsky, M. L. (Ed.). (1968). Semantic Information Processing. MIT Press, Cambridge, Massachusetts.

Minsky, M. L. (1975). A framework for representing knowledge. In Winston, P. H. (Ed.), The Psychology of Computer Vision, pp. 211–277. McGraw-Hill, New York. Originally an MIT AI Laboratory memo; the 1975 version is abridged, but is the most widely cited.

Minsky, M. L. and Papert, S. (1969). Perceptrons: An Introduction to Computational Geometry (first edition). MIT Press, Cambridge, Massachusetts.

Minsky, M. L. and Papert, S. (1988). Perceptrons: An Introduction to Computational Geometry (Expanded edition). MIT Press, Cambridge, Massachusetts.

Minton, S. (1984). Constraint-based generalization: Learning game-playing plans from single expamples. In Proceedings of the National Conference on Artificial Intelligence (AAAI-84), pp. 251–254, Austin, Texas. Morgan Kaufmann.

Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), pp. 564–569, St. Paul, Minnesota. Morgan Kaufmann.

Minton, S., Johnston, M. D., Philips, A. B., and Laird, P. (1992). Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58(1–3), 161–205.

Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts.

Mitchell, M., Holland, J. H., and Forrest, S. (1996). When will a genetic algorithm outperform hill climbing?. In Cowan, J., Tesauro, G., and Alspector, J. (Eds.), Advances in Neural Information Processing Systems, Vol. 6. MIT Press, Cambridge, Massachusetts.

Mitchell, T. M. (1977). Version spaces: A candidate elimination approach to rule learning. In Proceedings of the Fifth Intgernational Joint Conference on Artificial Intelligence (IJCAI-77), pp. 305–310, Cambridge, Massachusetts. IJCAII.

Mitchell, T. M. (1982). Generalization as search. Artificial Intelligence, 18(2), 203–226.

Mitchell, t. M. (1990). Becoming increasingly reactive (mobile robots). In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), Vol. 2, pp. 1051–1058, Boston. MIT Press.

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, New York.

Mitchell, t. M., Keller, R., and Kedar-Cabelli, S. (1986). Explanation-based generalization: A unifying view. Machine Learning, 1, 47–80.

Mitchell, T. M., Utgoff, P. E., and Banerji, r. (1983). Learning by experimentation: Acquiring and refining problem-solving heuristics. In Michalski, r. s., Carbonell, J. G., and Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach, pp. 163–190. Morgan Kaufmann, San Mateo, California.

Mitkov, R. (2002). Anaphora Resolution. Longman, New York.

Mohr, R. and Henderson, T. C. (1986). Arc and path consistency revisited. Artificial Intelligence, 28(2), 225–233.

Mohri, M., Pereira, F., and Riley, M. (2002). Weighted finite-state transducers in speech recognition. Computer Speech and Language, 16(1), 69–88.

Montague, P. r., Dayan, P., Person, C., and Sejnowsky, T. (1995). Bee foraging in uncertain environments using predictive Hebbian learning. Nature, 377, 725–728.

Montague, R. (1970). English as a formal language. In Linguaggi nella Società e nella Tecnica, pp. 189–224. Edizioni di Comunità, Milan.

Montague, r. (1973). The proper treatment of quantification in ordinary English. In Hintikka, K. J. J., Moravcsik, J. M. E., and Suppes, P. (Eds.), Approaches to Natural Language. D. Reidel, Dordrecht, Netherlands.

Montanari, U. (1974). Networks of constraints: Fundamental properties and applications to picture processing. Information Sciences, 7(2), 95–132.

Montemerlo, M. Thrun, S., Koller, D., and Wegbreit, B. (2002). FastSLAM: A factored solution to the simultaneous localization and mapping problem. In Preceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), Edmonton, Alberta. AAAI Press.

Mooney, R. (1999). Learning for semantic interpretation: Scaling up without dumbing down. In Cussens, J. (Ed.), Proceedings of the 1st Workshop on Learning Language in Logic, pp. 7–15. Springer-Verlag.

Mooney, R. J. and Califf, M. E. (1995). Induction of first-order decision lists: Results on learning the past tense of English verbs. Journal of AI Research, 3, 1–24.

Moore, A. W. and Atkeson, C. G. (1993). Prioritized sweeping—reinforcement learning with less data and less time. Machine Learning, 13, 103–130.

Moore, E. F. (1959). The shortest path through a maze. In Proceedings of an International Symposium on the Theory orf Switching, Part II, pp. 285–292. Harvard University Press, Cambridge, Massachusetts.

Moore, J. S. and Newell, A. (1973). How can Merlin understand?. In Gregg, L. (Ed.), Knowledge and Cognition. Lawrence Erlbaum Associates, Potomac, Maryland.

Moore, R. C. (1980). Reasoning about knowledge and action. Artificial intelligence center technical note 191, SRI Inernational, Menlo Park, California.

Moore, R. C. (1985). A formal theory of knowledge and action. In Hobbs, J. R. and Moore, R. C. (Eds.), Formal Theories of the Commonsense World, pp. 319–358. Ablex, Norwood, New Jersey.

Moravec, H. P. (1983). The stanford cart and the cmu rover. Proceedings of the IEEE, 71(7), 872–884.

Moravec, H., P. and Elfes, A. (1985). High resolution maps from wide angle sonar. In 1985 IEEE International Conference on Robotics and Automation, pp. 116–121, St. Louis, Missouri. IEEE Computer Society Press.

Moravec, H. P. (1988). Mind Children: The Future of Robot and Human Intelligence. Harvard Unviersity Press, Cambridge, Massachusetts.

Moravec, H. P. (2000). Robot: Mere Machine to Transcendent Mind. Oxford University Press.

Morgenstern, L. (1987). Knowledge preconditions for actions and plans. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), pp. 867–874, Milan. Morgan Kaufmann.

Morgenstern, L. (1998). Inheritance comes of age: Applying nonmonotonic techniques to problems in industry. Artificial Intelligence, 103, 237–271.

Morjaria, M. A., Rink, F. J., Smith, W. D., Klempner, G., Burns, C., and Stein, J. (1995). Elicitation of probabilities for belief networks: Combining qualitative and quantitative information. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 141–148. Morgan Kaufmann.

Morrison, P. and Morrison, E. (Eds.). (1961). Charles Babbage and His Calculating Engines: Selected Writings by Charles Babbage and Others. Dover, New York.

Moskewicz, M. W., Madigan, C. F., Zhao, Y., Zhang, L., and Malik, S. (2001). Chaff: Engineering an efficient SAT solver. In Proceedings of the 38th Design Automation Conference (DAC 2001), pp. 530–535. ACM Press.

Mosteller, F. and Wallace, D. L. (1964). Inference and Disputed Authorship: The Federalist. Addison-Wesley.

Mostow, J. and Prieditis, A. E. (1989). Discovering admissible heuristics by abstracting and optimizing: A transformational approach. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Vol. 1, pp. 701–707, Detroit. Morgan Kaufmann.

Motzkin, T. S. and Schoenberg, I. J. (1954). The relaxation method for linear inequalities. Canadian Journal of Mathematics, 6(3), 393–404.

Moussouris, J., Holloway, J., and Greenblatt, R. D. (1979). CHEOPS: A chess-oriented processing system. In Hayes, J. E., Michie, D., and Mikulich, L. I. (Eds.), Machine Intelligence 9, pp. 351–360. Ellis Horwood, Chichester, England.

Moutarlier, P. and Chatila, R. (1989). Stochastic multisensory data fusion for mobile robot location and environment modeling. In 5th Int. Symposium on Robotics Research, Tokyo.

Muggleton, S. H. (1991). Inductive logic programming. New Generation Computing, 8, 295–318.

Muggleton, s. H. (1992). Inductive Logic Programming. Academic Press, New York.

Muggleton, S. H. (1995). Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming, 13(3–4), 245–286.

Muggleton, S. H. (2000). Learning stochastic logic programs. Proceedings of the AAAI 2000 Workshop on Learning Statistical Models from Relational Data.

Muggleton, S. H. and Buntine, W. (1988). Machine invention of first-order predicates by inverting resolution. In Proceedings of the Fifth International Conference on Machine Learning, pp. 339–352. Morgan Kaufmann.

Muggleton, S. H. and De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20, 629–679.

Muggleton, s. H. and Feng, C. (1990). Efficient induction of logic programs. In Proceedings of the Workshop on Algorithmic Learning Theory, pp. 368–381, Tokyo. Ohmsha.

Müller, M. (2002). Computer Go. Artificial Intelligence, 134(1–2), 145–179.

Mundy, J. and Zisserman, A. (Eds.). (1992). Geometric Invariance in Computer Vision. MIT Press, Cambridge, Massachusetts.

Murphy, K., Weiss, Y., and Jordan, M. I. (1999). Loopy belief propagation for approximate inference: An empirical study. In Uncertainty in Artificial Intelligence: Proceedings of the Fifteenth Conference, pp. 467–475, Stockholm. Morgan Kaufmann.

Murphy, K. and Russell, S. J. (2001). Raoblackwellised particle filtering for dynamic bayesian networks. In Doucet, A., de Freitas, N., and Gordon, N. J. (Eds.), Sequential Monte Carlo Methods in Practice. Springer-Verlag, Berlin.

Murphy, R. (2000). Introduction to AI Robotics. MIT Press, Cambridge, Massachusetts.

Muscettola, N., Nayak, P., Pell, B., and Williams, B. (1998). Remote agent: To boldly go where no ai system has gone before. Artificial Intelligence, 103, 5–48.

Myerson, r. B. (1991). Game Theory: Analysis of Conflict. Harvard University Press, Cambridge.

Nagel, t. (1974). What is it like to be a bat?. Philosophical Review, 83, 435–450.

Nalwa, V. S. (1993). A Guided Tour of Computer Vision. Addison-Wesley, Reading, Massachusetts.

Nash, J. (1950). Equilibrium points in N-person games. Proceedings of the National Academy of Sciences of the United States of America, 36, 48–49.

Nau, D. S. (1980). Pathology on game trees: A summary of results. In Proceedings of the First Annual National Conference on Artificial Intelligence (AAAI-80), pp. 102–104, Stanford, California. AAAI.

Nau, d. S. (1983). Pathology on game trees revisited, and an alternative to minimaxing. Artificial Intelligence, 21(1–2), 221–244.

Nau, D. S., Kumar, V., and Kanal, L. N. (1984). General branch and bound, and its relation to A* and AO*. Artificial Intelligence, 23, 29–58.

Naur, P. (1963). Revised report on the algorithmic language Algol 60. Communications of the Association for Computing Machinery, 6 (1), 1–17.

Nayak, P. and Williams, B. (1997). Fast context switching in real-time propositional reasoning. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), pp. 50–56, Providence, Rhode Island. AAAI Press.

Neal, R. (1996). Bayesian Learning for Neural Networks. Springer-Verlag, Berlin.

Nebel, B. (2000). On the compilability and expressive power of propositional planning formalisms. Journal of AI Research, 12, 271–315.

Nelson, G. and Oppen, D. C. (1979). Simplification by cooperating decision procedures. ACM Transactions on Programming Languages and Systems, 1(2), 245–257.

Netto, E. (1901). Lehrbuch der Combinatorik. b. G. Teubner, Leipzig.

Nevill-Manning, C. G. and Witten, I. H. (1997). Identifying hierarchical structures in sequences: A linear-time algorithm. Journal of AI Research, 7, 67–82.

Nevins, A. J. (1975). Plane geometry theorem proving using forward chaining. Artificial Intelligence, 6(1), 1–23.

Newell, a. (1982). The knowledge level. Artificial Intelligence, 18(1), 82–127.

Newell, A. (1990). Unified Theories of Cognition. Harvard University Press, Cambridge, Massachusetts.

Newell, A. and Ernst, G. (1965). The search for generality. In Kalenich, W. A. (Ed.), Information Processing 1965: Proceedings of IFIP Congress 1965, Vol. 1, pp. 17–24, Chicago. Spartan.

Newell, A., Shaw, J. C., and Simon, H. A. (1957). Empirical explorations with the logic theory machine. Proceedings of the Western Joint Computer Conference, 15, 218–239. Reprinted in Feigenbaum and Feldman (1963).

Newell, A., Shaw, J. C., and Simon, H. A. (1958). Chess playing programs and the problem of complexity. IBM Journal of Research and Development, 4(2), 320–335.

Newell, A. and Simon, H. a. (1961). GPS, a program that simulates human thought. In Billing, H. (Ed.), Lernende Automaten, pp. 109–124. R. Oldenbourg, Munich.

Newell, A. and Simon, H. A. (1972). Human Problem Solving. Prentice-Hall, Upper Saddle River, New Jersey.

Newell, A. and Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the Association for Computing Machinery, 19, 113–126.

Newton, I. (1664–1671). Methodus fluxionum et serierum infinitarum. Unpublished notes.

Ng, A. Y., Harada, D., and Russell, S. J. (1999). Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia. Morgan Kaufmann.

Ng, A. Y. and Jordan, M. I. (2000). PEGASUS: A policy search method for large MDPs and POMDPs. In Uncertainty in Artificial Intelligence: proceedings of the Sixteenth Conference, pp. 406–415, Stanford, California. Morgan Kaufmann.

Nguyen, X. and Kambhampati, S. (2001). Reviving partial order planning. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 459–466, Seattle. Morgan Kaufmann.

Nguyen, X. Kambhampati, S., and Nigenda, R. S. (2001). Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search. Tech. rep., Computer Science and Engineering Department, Arizona State University.

Nicholson, A. and Brady, J. M. (1992). The data association problem when monitoring robot vehicles using dynamic belief networks. In ECAI 92: 10th European Conference on Artificial Intelligence Proceedings, pp. 689–693, Vienna, Austria. Wiley.

Niemelä, I., Simons, P., and Syrjänen, T. (2000). Smodels: A system for answer set programming. In Proceedings of the 8th International Workshop on Non-Monotonic Reasoning.

Nilsson, D. and Lauritzen, S. (2000). Evaluating influence diagrams using LIMIDs. In Uncertainty in Artificial Intelligence: Proceedings of the Sixteenth Conference, pp. 436–445, Stanford, California. Morgan Kaufmann.

Nilsson, N. J. (1965). Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw-Hill, New York. republished in 1990.

Nilsson, N. J. (1971). Problem-Solving Methods in Artificial Intelligence. McGraw-Hill, New York.

Nilsson, N. J. (1980). Principles of Artificial Intelligence. Morgan Kaufmann, San Mateo, California.

Nilsson, N. J. (1984). Shakey the robot. Technical note 323, SRI International, Menlo Park, California.

Nilsson, N. J. (1986). Probabilistic logic. Artificial Intelligence, 28(1), 71–87.

Nilsson, N. J. (1991). Logic and artificial intelligence. Artificial Intelligence, 47(1–3), 31–56.

Nilsson, N. J. (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann, San Mateo, California.

Norvig, P. (1988). Multiple simultaneous interpretations of ambiguous sentences. In Proceedings of the 10th Annual Conference of the Cognitive Science Society.

Norvig, P. (1992). Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp. Morgan Kaufmann, San Mateo, California.

Nowick, S. M., Dean, M. E., Dill, D. L., and Horowitz, M. (1993). The design of a high-performance cache controller: A case study in asynchronous synthesis. Integration: The VLSI Journal, 15(3), 241–262.

Nunberg, g. (1979). The non-uniqueness of semantic solutions: Polysemy. Language and Philosophy, 3(2) 143–184.

Nussbaum, M. C. (1978). Aristotle’s De Motu Animalium. Princeton University Press, Princeton, New Jersey.

Ogawa, S., Lee, t.-M., Kay, A. R., and Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87, 9868–9872.

Olawsky, D. and Gini, M. (1990). Deferred planning and sensor use. In Sycara, K. P. (Ed.), Proceedings, DARPA Workshop on Innovative Approaches to Planning, Scheduling, and Control, San Diego, California. Defense Advanced Research Projects Agency (DARPA), Morgan Kaufmann.

Olesen, K. G. (1993). Causal probabilistic networks with both discrete and continuous variables. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 15(3), 275–279.

Oliver, r. M. and Smith, J. Q. (Eds.). (1990). Influence Diagrams, Belief Nets and Decision Analysis. Wiley, New York.

Olson, C. F. (1994). Time and space efficient pose clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 251–258, Washington, DC. IEEE Computer Society Press.

Oncina, J. and Garcia, P. (1992). Inferring regular languages in polynomial update time. In Perez, Sanfeliu, and Vidal (Eds.), Pattern Recognition and Image Analysis, pp. 49–61. World Scientific.

O’Reilly, U.-M. and Oppacher, f. (1994). Program search with a hierarchical variable length representation: Genetic programming, simulated annealing and hill climbing. In Davidor, Y., Schwefel, H.-P., and Manner, R. (Eds.), Proceedings of the Third Conference on Parallel Problem Solving from Nature, pp. 397–406, Jerusalem, Israel. Springer-Verlag.

Ormoneit, D. and Sen, S. (2002). Kernel-based reinforcement learning. Machine Learning, 49(2–3), 161–178.

Ortony, A. (Ed.). (1979). Metaphor and Thought. Cambridge University Press, Cambridge, UK.

Osborne, M. J. and Rubinstein, A. (1994). A Course in Game Theory. MIT Press, Cambridge, Massachusetts.

Osherson, D. N., Stob, M., and Weinstein, S. (1986). Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge, Massachusetts.

Page, C. D. and Srinivasan, A. (2002). ILP: A short look back and a longer look forward. Submitted to Journal of Machine Learning Research.

Pak, I. (2001). On mixing of certain random walks, cutoff phenomenon and sharp threshold of random matroid processes. DAMATH: Discrete Applied Mathematics and Combinatorial Operations Research and Computer Science, 110, 251–272.

Palay, A. J. (1985). Searching with Probabilities. Pitman, London.

Palmer, D. A. and Hearst, M. A. (1994). Adaptive sentence boundary disambiguation. In Proceedings of the Conference on Applied Natural Language Processing, pp. 78–83. Morgan Kaufmann.

Palmer, S. (1999). Vision Science: Photons to Phenomenology. MIT Press, Cambridge, Massachusetts.

Papadimitriou, c. H. (1994). Computational Complexity. Addison-Wesley.

Papadimitriou, C. H., Tamaki, H., Raghavan, P., and Vempala, s. (1998). Latent semantic indexing: A probabilistic analysis. In Proceedings of the ACM Conference on Principles of Database Systems (PODS), pp. 159–168, New York. ACM Press.

Papadimitriou, C. H. and Tsitsiklis, J. N. (1987). The complexity of markov decision processes. Mathematics of Operations Research, 12(3), 441– 450.

Papadimitriou, C. H. and Yannakakis, M. (1991). Shortest paths without a map. Theoretical Computer Science, 84(1), 127–150.

Papavassiliou, V. and Russell, S. J. (1999). Convergence of reinforcement learning with general function approximators. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 748–757, Stockholm. Morgan Kaufmann.

Parekh, R. and Honavar, V. (2001). Dfa learning from simple examples. Machine Learning, 44, 9–35.

Parisi, g. (1988). Statistical field theory. Addison-Wesley, Reading, Massachusetts.

Parker, D. B. (1985). Learning logic. Technical report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, Masachusetts.

Parker, L. E. (1996). On the design of behavior-based multi-robot teams. Journal of Advanced Robotics, 10(6).

Parr, R. and Russell, S. J. (1998). Reinforcement learning with hierarchies of machines. In Jordan, M. I., Kearns, M., and Solla, S. A. (Eds.), Advances in Neural Information Processing Systems 10. MIT Press, Cambridge, Massachusetts.

Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065–1076.

Pasula, H. and Russell, S. J. (2001). Approximate inference for first-order probabilistic languages. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), Seattle. Morgan Kaufmann.

Pasula, H., Russell, S. J., Ostland, M., and Ritov, Y. (1999). Tracking many objects with many sensors. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm. Morgan Kaufmann.

Paterson, M. S. and Wegman, M. N. (1978). Linear unification. Journal of Computer and System Sciences, 16, 158–167.

Patrick, B. G., Almulla, M., and Newborn, M. M. (1992). An upper bound on the time complexity of iterative-deepening-A*. Annals of Mathematics and Artificial Intelligence, 5(2–4), 265–278.

Patten, T. (1988). Systemic Text Generation as Problem Solving. Studies in Natural Language Processing. Cambridge University Press, Cambridge, UK.

Paul, R. P. (1981). Robot Manipulators: Mathematics, Programming, and Control. MIT Press, Cambridge, Massachusetts.

Peano, G. (1889). Arithmetices principia, nova methodo exposita. Fratres Bocca, Turin.

Pearl, J. (1982a). Reverend Bayes on inference engines: A distributed hierarchical approach. In Proceedings of the National Conference on Artificial Intelligence (AAAI-82), pp. 133–136, Pittsburgh, Pennsylvania. Morgan Kaufmann.

Pearl, J. (1982b). The solution for the branching factor of the alpha–beta pruning algorithm and its optimality. Communications of the Association for Computing Machinery, 258), 559–564.

Pearl, J. (1984). Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading, Massachusetts.

Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29, 241–288.

Pearl, J. (1987). Evidential reasoning using stochastic simulation of causal models. Artificial Intelligence, 32, 247–257.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, California.

Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK.

Pearl, J. and Verma, T. (1991). A theory of inferred causation. In Allen, J. A., Fikes, R., and Sandewall, E. (Eds.), Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, pp. 441–452, San Mateo, California. Morgan Kaufmann.

Pearson, J. and Jeavons, P. (1997). A survey of tractable constraint satisfaction problems. Technical report CSD-TR-97-15, Royal Holloway College, U. of London.

Pednault, E. P. D. (1986). Formulating multiagent, dynamic-world problems in the classical planning framework. In Georgeff, M. P. and Lansky, A. L. (Eds.), Reasoning about Actions and Plans: Proceedings of the 1986 Workshop, pp. 47–82, Timberline, Oregon. Morgan Kaufmann.

Peirce, C. S. (1870). Description of a notation for the logic of relatives, resulting from an amplification of the conceptions of Boole’s calculus of logic. Memoirs of the American Academy of Arts and Sciences, 9, 317–378.

Peirce, C. S. (1883). A theory of probable inference. Note B. The logic of relatives. In Studies in Logic by Members of the Johns Hopkins University, pp. 187–203, Boston.

Peirce, C. S. (1902). Logic as semiotic: The theory of signs. Unpublished manuscript; reprinted in (Buchler 1955).

Peirce, C. S. (1909). Existential graphs. Unpublished manuscript; reprinted in (Bucler 1955).

Pelikan, M., Goldberg, D. E., and Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 525–532, Orlando, Florida. Morgan Kaufmann.

Pemberton, J. C. and Korf, R. E. (1992). Incremental planning on graphs with cycles. In Hendler, J. (Ed.), Artificial Intelligence Planning Systems: Proceedings of the First International Conference, pp. 525– 532, College Park, Maryland. Morgan Kaufmann.

Penberthy, J. S. and Weld, D. S. (1922). UCPOP: A sound, complete, partial order planner for ADL. In Proceedings of KR-92, pp. 103–114. Morgan Kaufmann.

Peng, J. and Williams, R. J. (1993). Efficient learning and planning within the Dyna framework. Adaptive Behavior, 2, 437–454.

Penrose, R. (1989). The Emperor’s New Mind. Oxford University Press, Oxford, UK.

Penrose, R. (1994). Shadows of the Mind. Oxford University Press, Oxford, UK.

Peot, M. and Smith, D. E. (1992). Conditional nonlinear planning. In Hendler, J. (Ed.), Proceedings of the First International Conference on AI Planning Systems, pp. 189–197, College Park, Maryland. Morgan Kaufmann.

Pereira, F. and Shieber, S. M. (1987). Prolog and Natural-Language Analysis. Center for the Study of Language and Information (CSLI), Stanford, California.

Pereira, f. and Warren, D. H. D. (1980). Definite clause grammars for language analysis: A survey of the formalism and a comparison with augmented transition networks. Artificial Intelligence, 13, 231–278.

Peterson, C. and Anderson, J. R. (1987). A mean field theory learning algorithm for neural networks. Complex Systems, 1(5) 995–1019.

Pfeffer, A. (2000). Probabilistic Reasoning for Complex Systems. Ph.D. thesis, Stanford University, Stanford, California.

Pinker, S. (1989). Learnability and Cognition. MIT Press, Cambridge, MA.

Pinker, s. (1995). Language acquisition. In Gleitman, L. R., Liberman, M., and Osherson, D. N. (Eds.), An Invitation to Cognitive Science (second edition)., Vol. 1. MIT Press, Cambridge Massachusetts.

Pinker, S. (2000). The Language Instinct: How the Mind Creates Language. MIT Press, Cambridge, Massachusetts.

Plaat, A., Schaeffer, J., Pijls, W., and de Bruin, A. (1996). Best-first fixed-depth minimax algorithms. Artificial Intelligence Journal, 87 (1–2), 255–293.

Place, U. T. (1956). Is consciousness a brain process?. British Journal of Psychology, 47, 44–50.

Plotkin, g. (1971). Automatic Methods of Inductive Inference. Ph.D. thesis, Edinburgh University.

Plotkin, G. (1972). Building-in equational theories. In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 7, pp. 73–90. Edinburgh University Press, Edinburgh, Scotland.

Pnueli, A. (1977). The temporal logic of programs. In Proceedings of the 18th IEEE Symposium on the Foundations of Computer Science (FOCS-77), pp. 46–57, Providence, Rhode Island. IEEE, IEEE Computer Society Press.

Pohl, I. (1969). Bi-directional and heuristic search in path problems. Tech. rep. 104, SLAC (Stanford Linear Accelerator Center), Stanford, California.

Pohl, I. (1970). First results on the effect of error in heuristic search. In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 5, pp. 219–236. Elsevier/North-Holland, Amsterdam, London, New York.

Pohl, I. (1971). Bi-directional search. In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 6, p. 127–140. Edinburgh University Press, Edinburgh, Scotland.

Pohl, I. (1973). The avoidance of (relative) catastrophe, heuristic competence, genuine dynamic weighting and computational issues in heuristic problem solving. In Proceedings of the Third International Joint Conference on Artificial Intelligence (IJCAI-73), pp. 20–23, Stanford, California. IJCAII.

Pohl, I. (1977). Practical and theoretical considerations in heuristic search algorithms. In Elcock, E. W. and Michie, D. (Eds.), Machine Intelligence 8, pp. 55–72. Ellis Horwood, Chichester, England.

Pomerleau, d. A. (1993). Neural Network Perception for Mobile Robot Guidance. Kluwer, Dordrecht, Netherlands.

Ponte, J. M. and Croft, W. B. (1998). A language modeling approach to information retrieval. In Research and Development in Information Retrieval, pp. 275–281.

Poole, D. (1993). Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64, 81–129.

Poole, D., Mackworth, A. K., and Goebel, R. (1998). Computational intelligence: A logical approach. Oxyford University Press, Oxford, UK.

Popper, K. R. (1959). The Logic of Scientific Discovery. Basic Books, New York.

Popper, K. R. (1962). Conjectures and Refutations: The Growth of Scientific Knowledge. Basic Books, New York.

Porter, M. F. (1980). An algorithm for suffix stripping. Program, 13(3), 130–137.

Post, E. L. (1921). Introduction to a general theory of elementary propositions. American Journal of Mathematics, 43, 163–185.

Pradhan, M., Provan, G. M., Middleton, B., and Henrion, M. (1994). Knowledge engineering for large belief networks. In Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference, pp. 484–490, Seattle, Washington, Morgan Kaufmann.

Pratt, V. R. (1976). Semantical considerations on Floyd-Hoare logic. In Proceedings of the 17th IEEE Symposium on the Foundations of Computer Science, pp. 109–121. IEEE Computer Society Press.

Prawitz, d. (1960). An improved proof procedure. Theoria, 26, 102–139.

Prawitz, D. (1965). Natural Deduction: A Proof Theoretical Study. Almquist and Wiksell, Stockholm.

Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (2002). Numerical Recipes in C++: The Art of Scientific Computing (Second edition). Cambridge University Press, Cambridge, UK.

Prieditis, A. E. (1993). Machine discovery of effective admissible heuristics. Machine Learning, 12(1–3), 117–141.

Prinz, D. G. (1952). Robot chess. Research, 5, 261–266.

Prior, A. N. (1967). Past, Present, and Future. Oxford University Press, Oxford, UK.

Prosser, P. (1993). Hybrid algorithms for constraint satisfaction problems. Computational Intelligence, 9, 268–299.

Pryor, L. and Collins, G. (1996). Planning for contingencies: A decision-based approach. Journal of Artificial Intelligence Research, 4, 287–339.

Pullum, g. K. (1991). The Great Eskimo Vocabulary Hoax (and Other Irreverent Essays on the Study of Language). University of Chicago Press, Chicago.

Pullum, G. K. (1996). Learnability, hyperlearning, and the poverty of the stimulus. In 22nd Annual Meeting of the Berkeley Linguistics Society.

Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York.

Puterman, M. L. and Shin, M. C. (1978). Modified policy iteration algorithms for discounted Markov decision problems. Management Science, 24(11), 1127–1137.

Putnam, H. (1960). Minds and machines. In Hook, S. (Ed.), Dimensions of Mind, pp. 138–164. Macmillan, London.

Putnam, H. (1963). ’Degree of confirmation’ and inductive logic. In Schilpp, P. A. (Ed.), The Philosophy of Rudolf Carnap, pp. 270–292. Open Court, La Salle, Illinois.

Putnam, H. (1967). The nature of mental states. In Capitan, W. H. and Merrill, D. D. (Eds.), Art, Mind, and Religion, pp. 37–48. University of Pittsburgh Press, Pittsburgh.

Pylyshyn, Z. W. (1974). Minds, machines and phenomenology: Some reflections on Dreyfus’ „What Computers Can’t Do”. International Journal of Cognitive Psychology, 3(1), 57–77.

Pylyshyn, Z. W. (1984). Computation and Cognition: Toward a Foundation for Cognitive Science. MIT Press, Cambridge, Massachusetts.

Quillian, R. (1961). A design for an understanding machine. Paper presented at a colloquium: Semantic Problems in Natural Language, King’s College, Cambridge, England.

Quine, W. V. (1953). Two dogmas of empiricism. In From a Logical Point of View, pp. 20–46. Harper and Row, New York.

Quine, W. V. (1960). Word and Object. MIT Press, Cambridge, Massachusetts.

Quine, W. V. (1982). Methods of Logic (Fourth edition). Harvard University Press, Cambridge, Massachusetts.

Quinlan, e. and O’Brien, S. (1992). Sublanguage: Characteristics and selection guidelines for MT. In AI and Cognitive Science ’92: Proceedings of Annual Irish Conference on Artificial Intelligence and Cognitive Science ’92, pp. 342–345, Limerick, Ireland. Springer-Verlag.

Quinlan, J. R. (1979). Discovering rules from large collections of examples: A case study. In Michie, D. (Ed.), Expert Systems in the Microelectronic Age. Edinburgh University Press, Edinburgh, Scotland.

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.

Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5(3), 239–266.

Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, California.

Quinlan, J. R. and Cameron-Jones, R. M. (1993). FOIL: a midterm report. In Brazil, P. B. (Ed.), European Conference on Machine Learning Proceedings (ECML-93), pp. 3–20, Vienna. Springer-Verlag.

Quirk, R., Greenbaum, S., Leech, G., and Svartvik, J. (1985). A Comprehensive Grammar of the English Language. Longman, New York.

Rabani, Y. Rabinovich, Y., and Sinclair, A. (1998). A computational view of population genetics. Random Structures and Algorithms, 12(4), 313–334.

Rabiner, L. R. and Juang, B.-H. (1993). Fundamental os Speech Recognition. Prentice-Hall, Upper Saddle River, New Jersey.

Ramakrishnan, R. and Ullman, J. D. (1995). A survey of research in deductive database systems. Journal of Logic Programming, 23(2), 125–149.

Ramsey, F. P. (1931). Truth and probability. In Braithwaite, R. B. (Ed.), The Foundations of Mathematics and Other Logical Essays. Harcourt Brace Jovanovich, New York.

Raphson, J. (1690). Analysis aequationum universalis. Apud Abelem Swalle, London.

Rassenti, S., Smith, V., and Bulfin, R. (1982). A combinatorial auction mechanism for airport time slot allocation. Bell Journal of Economics, 13, 402–417.

Ratner, D. and Warmuth, M. (1986). Finding a shortest solution for th n × n extension of the 15-puzzle is intractable. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), Vol. 1, pp. 168–172, Philadelphia. Morgan Kaufmann.

Rauch, H. E., Tung, F., and Striebel, C. T. (1965). Maximum likelihood estimates of linear dynamic systems. AIAA Journal, 3(8), 1445–1450.

Rechenberg, I. (1965). Cybernetic solution path of an experimental problem. Library translation 1122, Royal Aircraft Establishment.

Rechenberg, I. (1973). Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart, Germany.

Regin, J. (1994). A filtering algorithm for constraints of difference in CSPs. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 362–367, Seattle. AAAI Press.

Reichenbach, H. (1949). The Theory of Probability: An Inquiry into the Logical and Mathematical Foundations of the Calculus of Probability (Second edition). University of California Press, Berkeley and Los Angeles.

Reif, J. (1979). Complexity of the mover’s problem and generalizations. In Proceedings of the 20th IEEE Symposium on Foundations of Computer Science, pp. 421–427, San Juan, Puero Rico. IEEE, IEEE Computer Society Press.

Reiter, E. and Dale, R. (2000). Building Natural Language Generation Systems. Studies in Natural Language Processing. Cambridge University Press, Cambridge, UK.

Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence, 13(1–2), 81–132.

Reiter, R. (1991). The frame problem in the situation calculus: A simple solution (sometimes) and a completeness result for goal regression. In Lifschitz, V. (Ed.), Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy, pp. 359–380. Academic Press, New York.

Reiter, R. (2001a). On knowledge-based programming with sensing in the situation calculus. ACM Transactions on Computational Logic, 2(4), 433– 457.

Reiter, R. (2001b). Knowledge in Action: Logical Foundations for Specifying Implementing Dynamical Systems. MIT Press, Cambridge, Massachusetts.

Reitman, W. and Wilcox, B. (1979). The structure and performance of the INTERIM.2 Go program. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence (IJCAI-79), pp. 711–719, Tokyo. IJCAII.

Remus, H. (1962). Simulation of a learning machine for playing Go. In Proceedings IFIP Congress, pp. 428–432, Amsterdam, London, New York. Elsevier/North-Holland.

Rényi, A. )1970). Probability Theory. Elsevier/North-Holland, Amsterdam, London, New York.

Rescher, N. and Urquhart, A. (1971). Temporal Logic. Springer-Verlag, Berlin.

Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model. Computer Graphics, 21, 25–34. SIGGRAPH ’87 Conference Proceedings.

Rich, E. and Knight, K. (1991). Artificial Intelligence (second edition). McGraw-Hill, New York.

Richardson, M., Bilmes, J., and Diorio, C. (2000). Hidden-articulator Markov models: Performance improvements and robustness to noise. In ICASSP-2000: 2000 International Conference on Acoustics, Speech, and Signal Processing, Los Alamitos, CA. IEEE Computer Society Press.

Rieger, C. (1976). An organization of knowledge for problem solving and language comprehension. Artificial Intelligence, 7, 89–127.

Ringle, M. (1979). Philosophical Perspectives in Artificial Intelligence. Humanities Press, Atlantic Highlands, New Jersey.

Rintanen, J. (1999). Improvements to the evaluation of quantified boolean formulae. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 1192–1197, Stockholm. Morgan Kaufmann.

Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK.

Rissanen, J. (1984). Universal coding, information, prediction, and estimation. IEEE Transactions on Information Theory, IT-30(4), 629–636.

Ritchie, G. D. and Hanna, F. K. (1984). AM: A case study in AI methodology. Artificial Intelligence, 23(3), 249–268.

Rivest, R. (1987). Learning decision lists. Machine Learning, 2(3), 229–246.

Roberts, L. G. (1963). Machine perception of three-dimensional solids. Technical report 315, MIT Lincoln Laboratory.

Robertson, N. and Seymour, P. D. (1986). Graph minors. ii. Algorithmic aspects of tree-width. Journal of Algorithms, 7(3), 309–322.

Robertson, S. E. (1977). The probability ranking principle in ir. Journal of Documentation, 33, 294–304.

Robertson, S. E. and Sparck Jones, K. (1976). Relevance weighting of search terms. Journal of the American Society for Information Science, 27, 129–146.

Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle. Journal of the Association for Computing Machinery, 12, 23–41.

Roche, E. and Schabes, Y. (1977). Finite-State Language Processing (Language, Speech and Communication). Bradford Books, Cambridge.

Rock, I. (1984). Perception. W. H. Freeman, New York.

Rorty, R. (1965). Mind-body identity, privacy, and categories. Review of Metaphysics, 19(1), 24–54.

Rosenblatt, F. (1957). The perception: A perceiving and recognizing automaton. Report 85-460-1, Project Para, Cornell Aeronautical Laboratory, Ithaca, New York.

Rosenblatt, F. (1960). On the convergence of reinforcement procedures in simple perceptrons. Report VG-1196-G-4, Cornell Aeronautical Laboratory, Ithaca, New York.

Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan, Chicago.

Rosenblatt, M. (1956). Remarks on some nonparametric estimates of a density function. Annals of Mathematical Statistics, 27, 832–837.

Rosenblueth, A., Wiener, N., and Bigelow, J. (1943). Behavior, purpose, and teleology. Philosophy of Science, 10, 18–24.

Rosenschein, J. S. and Zlotkin, G. (1994). Rules of Encounter. MIT Press, Cambridge, Massachusetts.

Rosenschein, S. J. (1985). Formal theories of knowledge in AI and robotics. New Generation Computing, 3(4), 345–357.

Rosenthal, D. M. (Ed.) (1971). Materialism and the Mind-Body Problem. Prentice-Hall, Upper Saddle River, New Jersey.

Ross, S. M. (1988). A First Course in Probability (third edition). Macmillan, London.

Roussel, P. (1975). Prolog: Manual de reference et d’utilization. Tech. ren. Groupe d’Intelligence Artificielle, Université d’Aix-Marseille.

Rouveirol, C. and Puget, J.-F. (1989). A simple and general solution for inverting resolution. In Proceedings of the European Working Session on Learning, pp. 201–210, Porto, Portugal Pitman.

Rowat, P. F. (1979). Representing the Spatial Experience and Solving Spatial problems in a Simulated Robot Environment. Ph.D. thesis, University of British Columbia, Vancouver, BC, Canada.

Roweis, S. T. and Ghahramani, U. (1999). A unifying review of Linear Gaussian Models. Neural Computation, 11(2), 305–345.

Rubin, D. (1988). Using the SIR algorithm to simulate posterior distributions. In Bernardo, J. M., de Groot, M. H., Lindley, D. V., and Smith, A. F. M. (Eds.), Bayesian Statistics 3, pp. 395–402. Oxford University Press, Oxford, UK.

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986a). Learning internal representations by error propagation. In Rumelhart, D. E. and McClelland, J. L. (Eds.), Parallel Distributed Processing. Vol. 1, chap. 8, pp. 318–362. MIT Press, Cambridge, Massachusetts.

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986b). Learning representations by backpropagating errors. Nature, 323, 533–536.

Rumelhart, D. E. and McClelland, J. L. (Eds.). (1986). Parallel Distributed Processing. MIT Press, Cambridge, Massachusetts.

Ruspini, E. H. Lowrance, J. D., and Strat, T. M. (1992). Understanding evidential reasoning. Intgernational Journal of Approximate Reasoning, 6(3), 401–424.

Russell, J. G. B. (1990). Is screening for abdominal aortic aneurysm worthwhile?. Clinical Radiology, 41, 182–184.

Russell, S. J. (1985). The compleat guide to MRS. Report STAN-CS-85-1080, Computer Science Department, Stanford University.

Russell, S. J. (1986). A quantitative analysis of analogy by similarity. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), pp. 284–288, Philadelphia. Morgan Kaufmann.

Russell, S. J. (1988). Tree-structured bias. In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), Vol. 2. pp. 641–645, St. Paul, Minnesota. Morgan Kaufmann.

Russell, S. J. (1992). Efficient memory-bounded search methods. In ECAI 92: 10th European Conference on Artificial Intelligence Proceedings, pp. 1–5, Vienna. Wiley.

Russell, S. J. (1998). Learning agents for uncertain environments (extended abstract). In Proceedings of the Eleventh Annual ACM Workshop on Computational Learning Theory (COLT-98), pp. 101–103, Madison, Wisconsin. ACM Press.

Russell, S. J., Binder, J., Koller, D., and Kanazawa, K. (1995). Local learning in probabilistic networks with hidden variables. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), pp. 1146–52, Montreal. Morgan Kaufmann.

Russell, S. J. and Grosof, B. (1987). A declarative approach to bias in concept learning. In Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI-87), Seattle. Morgan Kaufmann.

Russell, S. J. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice-Hall, Upper Saddle River, New Jersey.

Russell, S. J. and Subramanian, D. (1995). Provably bounded-optimal agents. Journal of Artificial Intelligence Research, 3, 575–609.

Russell, S. J., Subramanian, D., and Parr, R. (1993). Provably bounded optimal agents. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pp. 338–345, Chambery, France. Morgan Kaufmann.

Russell, S. J. and Wefald, E. H. (1989). On optimal game-tree search using rational meta-reasoning. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pp. 334–340, Detroit. Morgan Kaufmann.

Russell, S. J. and Wefald, E. H. (1991). Do the Right Thing: Studies in Limited Rationality. MIT Press, Cambridge, Massachusetts.

Rustagi, J. S. (1976). Variational Methods in Statistics. Academic Press, New York.

Ryder, J. L. (1971). Heuristic analysis of large trees as generated in the game of Go. Memo AIM-155, Stanford Artificial Intelligence Project, Computer Science Department, Stanford University, Stanford, California.

Sabin, D. and Freuder, E. C. (1994). Contradicting conventional wisdom in constraint satisfaction. In ECAI 94: 11th European Conference on Artificial Intelligence. Proceedings, pp. 125–129, Amsterdam. Wiley.

Sacerdoti, E. D. (1974). Planning in a hieararchy of abstraction spaces. Artificial Intelligence, 5(2), 115–135.

Sacerdoti, E. D. (1975). The nonlinear nature of plans. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence (IJCAI-75), pp. 206–214, Tbilisi, Georgia. IJCAII.

Sacerdoti, E. D. (1977). A Structure for Plans and Behavior. Elsevier/North-Holland, Amsterdam, London, New York.

Sacerdoti, E. D., Fikes, R. E., Reboh, R., Sagalowicz, D., Waldinger, R., and Wilber, B. M. (1976). QLISP – a language for the interactive development of complex systems. In Proceedings of the AFIPS National Computer Conference, pp. 349–356.

Sacks, E. and Joskowicz, L. (1993). Automated modeling and kinematic simulation of mechanisms. Computer Aided Design, 25(2), 106–118.

Sadri, F. and Kowalski, R. (1995). Variants of the event calculus. In International Conference on Logic Programming, pp. 67–81.

Sag, I. and Wasow, T. (1999). Syntactic Theory: An Introduction. CSLI Publications, Stanford, California.

Sager, N. (1981). Natural Language Information Processing: A Computer Grammar of English and 1st Applications. Addison-Wesley, Reading, Massachusetts.

Sahami, M., Dumais, S. T., Heckerman, D., and Horvitz, E. J. (1998). A Bayesian approach to filtering junk E-mail. In Learning for Text Categorization: Papers from the 1998 Workshop, Madison, Wisconsin. AAAI Technical Report WS-98-05.

Sahami, M., Hearst, M. A., and Saund, E. (1996). Applying the multiple cause mixture model to text categorization. In Saitta, L. (Ed.), Proceedings of ICML-96, 13th International Conference on Machine Learning, pp. 435–443, Bari, Italy. Morgan Kaufmann Publishers.

Salomaa, A. (1969). Probabilistic and weighted grammars. Information and Control, 15, 529– 544.

Salton, G. and McGill, M. J. (1983). Introduction to Modern Information Retrieval. McGraw-Hill, New York, NY.

Salton, G., Wong, A., and Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–620.

Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210–229.

Samuel, A. L. (1967). Some studies in machine learning using the game of checkers II – Recent progress. IBM Journal of Research and Development, 11(6), 601–617.

Samuelsson, C. and Rayner, M. (1991). Quantitative evaluation of explanation-based learning as an optimization tool for a large-scale natural language system. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91), pp. 609–615, Sydney. Morgan Kaufmann.

Sato, T. and Kameya, Y. (1997). PRISM: A symbolic-statistical modeling language. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1330–1335, Nagoya, Japan. Morgan Kaufmann.

Saul, L. K., Jaakkola, T., and Jordan, M. I. (1996). Mean field theory for sigmoid belief networks. Journal of Artificial Intelligence Research, 4, 61–76.

Savage, L. J. (1954). The Foundations of Statistics. Wiley, New York.

Sayere, K. (1993). Three more flaws in the computation model. Paper presented at the APA (Central Division) Annual Conference, Chicago, Illinois.

Schabes, Y., Abeille, A., and Joshi, A. K. (1988). Parsing strategies with lexicalized grammars: Application to tree adjoining grammars. In Vargha, D. (Ed.), Proceedings of the 12th International Conference on Computational Linguistics (COLING), Vol. 2, pp. 578–583, Budapest, John von Neumann Society for Computer Science.

Schaeffer, J. (1997). One Jump Ahead: Challenging Human Supremacy in Checkers. Springer-Verlag, Berlin.

Schank, R. C. and Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding. Lawrence Erlbaum Associates, Potomac, Maryland.

Schank, R. C. and Riesbeck, C. (1981). Inside Computer Understanding: Five Programs Plus Miniatures. Lawrence Erlbaum Associates, Potomac, Maryland.

Schapire, R. E. (1999). Theoretical views of boosting and applications. In Algorithmic Learning Theory: Proceedings of the 10th International Conference (ALT’99), pp. 13–25. Springer-Verlag, Berlin.

Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227.

Schmolze, J. G. and Lipkis, T. A. (1983). Classification in the KL-ONE representation system. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83), pp. 330–332, Karlsruhe, Germany. Morgan Kaufmann.

Schofield, P. D. A. (1967). Complete solution of the eight puzzle. In Dale, E. and Michie, D. (Eds.), Machine Intelligence 2, pp. 125–133. Elsevier/North-Holland, Amsterdam, London, New York.

Schölkopf, B. and Smola, A. J. (2002). Learning with Kernels. MIT Press, Cambridge, Massachusetts.

Schöning, T. (1999). A probabilistic algorithm for k-SAT and constraint satisfaction problems. In 40th Annual Symposium on Foundations of Computer Science, pp. 410–414, New York. IEEE Computer Society Press.

Schoppers, M. J. (1987). Universal plans for reactive robots in unpredictable environments. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), pp. 1039– 1046, Milan. Morgan Kaufmann.

Schoppers, M. J. (1989). In defense of reaction plans as caches. AI Magazine, 10(4), 51–60.

Schröder, E. (1877). Der Operationskreis des Logikkalküls. B. G. Teubner, Leipzig.

Schultz, W., Dayan, P., and Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593.

Schütze, H. (1995). Ambiguity in Language Learning: Computational and Cognitive Models. Ph.D. thesis, Stanford University. Also published by CSLI Press, 1997.

Schwartz, J. T. Scharir, M., and Hopcroft, J. (1987). Planning, Geometry and Complexity of Robot Motion. Ablex Publishing Corporation, Norwood, NJ.

Schwartz, S. P. (Ed.). (1977). Naming, Necessity, and Natural Kinds. Cornell University Press, Ithaca, New York.

Scott, D. and Krauss, P. (1966). Assigning probabilities to logical formulas. In Hintikka, J. and Suppes, P. (Eds.), Aspects of Inductive Logic: North-Holland, Amsterdam.

Scriven, M. (1953). The mechanical concept of mind. Mind, 62, 230–240.

Searle, J. R. (1969). Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press, Cambridge, UK.

Searle, J. R. (1980). Minds, brains, and programs. Behavior and Brain Sciences, 3, 417–457.

Searle, J. R. (1984). Minds, Brains and Science. Harvard University Press, Cambridge, Massachusetts.

Searle, J. R. (1990). Is the brain’s mind a computer program?. Scientific American, 262, 26–31.

Searle, J. R. (1992). The Rediscovery of the Mind. MIT Press, Cambridge, Massachusetts.

Selman, B., Kautz, H., and Cohen, B. (1996). Local search strategies for satisfiability testing. In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 26, pp. 521–532. American Mathematical Society, Providence, Rhode Island.

Selman, B. and Levesque, H. J. (1993). The complexity of path-based defeasible inheritance. Artificial Intelligence, 62(2), 303–339.

Selman, B. Levesque, H. J., and Mitchell, D. (1992). A new method for solving hard satisfiability problems. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 440–446, San Jose. AAAI Press.

Shachter, R. D. (1986). Evaluating influence diagrams. Operations Research, 34, 871–882.

Shachter, R. D. (1998). Bayes-ball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In Uncertainty in Artificial Intelligence: Proceedings of the Fourtheenth Conference, pp. 480–487, Madison, Wisconsin. Morgan Kaufmann.

Shachter, R. D., D’Ambrosio, B., and Del Favero, B. A. (1990). Symbolic probabilistic inference in belief networks. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 126–131, Boston, MIT Press.

Shachter, R. D. and Kenley, C. R. (1989). Gaussian influence diagrams. Management Science, 35(5), 527–550.

Shachter, R. D. and Peot, M. (1989). Simulation approaches to general probabilistic inference on belief networks. In Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI-89), Windsor, Ontario, Morgan Kaufmann.

Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press, Princeton, New Jersey.

Shafer, G. and Pearl, J. (Eds.). (1990). Readings in Uncertain Reasoning. Morgan Kaufmann, San Mateo, California.

Shahookar, K. and Mazumder, P. (1991). VLSI cell placement techniques. Computing Surveys, 23(2), 143–220.

Shanahan, M. (1997). Solving the Frame Pproblem. MIT Press, Cambridge, Massachusetts.

Shanahan, M. (1999). The event calculus explained. In Wooldridge, M. J. and Veloso, M. (Eds.), Artificial Intelligence Today, pp. 409–430. Springer-Verlag, Berlin.

Shankar, N. (1986). Proof-Checking Metamathematics. Ph.D. thesis, Computer Science Department, University of Texas at Austin.

Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois Press, Urbana, Illinois.

Shannon, C. E. (1950). Programming a computer for playing chess. Philosophical Magazine, 41(4), 256–275.

Shapiro, E. (1981). An algorithm that infers theories from facts. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), p. 1064, Vancouver, British Columbia. Morgan Kaufmann.

Shapiro, S. C. (Ed.). (1992). Encyclopedia of Artificial Intelligence (second edition). Wiley, New York.

Shapley, S. (1953). Stochastic games. In Proceedings of the National Academy of Sciences, Vol. 39, pp. 1095–1100.

Shavlik, J. and Dietterich, T. (Eds.). (1990). Readings in Machine Learning. Morgan Kaufmann, San Mateo, California.

Shelley, M. (1818). Frankenstein: or, the Modern Prometheus. Pickering and Chatto.

Shenoy, P. P. (1989). A valuation-based language for expert systems. International Journal of Approximate Reasoning, 3(5), 383–411.

Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 22(8), 888–905.

Shoham, Y. (1987). Temporal logics in AI: Semantical and ontological considerations. Artificial Intelligence, 33(1), 89–104.

Shoham, Y. (1993). Agent-oriented programming. Artificial Intelligence, 60(1), 51–92.

Shoham, Y. (1994). Artificial Intelligence Techniques in Prolog. Morgan Kaufmann, San Mateo, California.

Shortliffe, E. H. (1976). Computer-Based Medical Consultations: MYCIN. Elsevier/North-Holland, Amsterdam, London, New York.

Shwe, M. and Cooper, G. (1991). An empirical analysis of likelihood-weighting simulation on a large, multiply connected medical belief networks. Computers and Biomedical Research, 1991(5), 453–475.

Siekmann, J. and Wrightson, G. (Eds.), (1983). Automation of Reasoning. Springer-Verlag, Berlin.

Sietsma, J. and Dow, R. J. F. (1988). Neural net pruning – why and how. In IEEE International Conference on Neural Networks, pp. 325–333, San Diego. IEEE.

Siklossy, L. and Dreussi, J. (1973). An efficient robot planner which generates its own procedures. In Proceedings of the Third International Joint Conference on Artificial Intelligence (IJCAI-73), pp. 423–430, Stanford, California. IJCAII.

Silverstein, C., Henzinger, M., Marais. H., and Moricz, M. (1998). Analysis of a very large altavista query log. Tech. rep. 1998–014, Digital Systems Research Center.

Simmons, R. and Koenig, S. (1995). Probabilistic robot navigation in partially observable environments. In Proceedings of IJCAI-95. pp. 1080– 1087, Montreal, Canada. IJCAI Inc.

Simmons, R. and Slocum, J. (1972). Generating english discourse from semantic networks. Communications of the ACM, 15(10), 891–905.

Simon, H. A. (1947). Administrative behavior. Macmillan, New York.

Simon, H. A. (1957). Models of Man: Social and Rational. John Wiley, New York.

Simon, H. A. (1963). Experiments with a heuristic compiler. Journal of the Association for Computing Machinery, 10, 493–506.

Simon, H. A. (1981). The Sciences of the Artificial (second edition). MIT Press, Cambridge, Massachusetts.

Simon, H. A. (1982). Models of Bounded Rationality, Volume 1. The MIT Press, Cambridge, Massachusetts.

Simon, H. A. and Newell, A. (1958). Heuristic problem solving: The next advance in operations research. Operations Research, 6, 1–10.

Simon, H. A. and Newell, A. (1961). Computer simulation of human thinking and problem solving. Datamation, June/July, 35–37.

Simon, J. C. and Dubois, O. (1989). Number of solutions to satisfiability instances – applications to knowledge bases. Int. J. Pattern Recognition and Artificial Intelligence, 3, 53–65.

Sirovitch, L. and Kirby, M. (1987). Low-dimensional procedure for characterization of human faces. Journal of the Optical Society of America A, 2, 586–591.

Skinner, B. F. (1953). Science and Human Behavior. Macmillan, London.

Skolem, T. (1920). Logisch-kombinatorische Untersuchungen über die Erfüllbarkeit oder Beweisbarkeit mathematischer Sätze nebst einem Theoreme über die dichte Mengen. Videnskapsselskapetes skrifter, I. Matematisk-naturvidenskabelig klasse, 4.

Skolem, T. (1928). Über die mathematische Logik. Norsk matematisk tidsskrift, 10, 125–142.

Slagle, J. R. (1963a). A heuristic program that solves symbolic integration problems in freshman calculus. Journal of the Association for Computing Machinery, 10(4).

Slagle, J. R. (1963b). Game trees, m & n minimaxing, and the m & n alpha-beta procedure. Artificial intelligence group report 3, University of California, Lawrence Radiation Laboratory, Livermore, California.

Slagle, J. R. and Dixon, J. K. (1969). Experiments with some programs that search game trees. Journal of the Association for Computing Machinery, 16(2), 189–207.

Slate, D. J. and Atkin, L. R. (1977). CHESS 4.5 – Northwestern University chess program. In Frey, P. W. (Ed.), Chess Skill in Man and Machine, pp. 82–118. Springer-Verlag, Berlin.

Slater, E. (1950). Statistics for the chess computer and the factor of mobility. In Symposiumon Information Theory, pp. 150–152, London,. Ministry of Supply.

Sleator, D. and Temperley, D. (1993). Parsing English with a link grammar. In Third Annual Workshop on Parsing technologies.

Sloman, A. (1978). The Computer Revolution in Philosophy. Harvester Press, Hassocks, Sussex, UK.

Smallwood, R. D. and Sondik, E. J. (1973). The optimal control of partially observable Markov processes over a finite horizon. Operations Research, 21, 1071–1088.

Smith, D. E., Genesereth, M. R., and Ginsberg, M. L. (1986). Controlling recursive inference. Artificial Intelligence, 30(3), 343–389.

Smith, D. R. (1990). KIDS: a semiautomatic program development system. IEEE Transactions on Software Engineering, 16(9), 1024–1043.

Smith, D. R. (1996). Machine suppurt for software development. In Proceedings of the 18th International Conference on Software Engineering, pp. 167–168, Berlin. IEEE Computer Society Press.

Smith, D. E. and Weld, D. S. (1998). Conformant Graphplan. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 889–896, Madison, Wisconsin. AAAI Press.

Smith, J. Q. (1988). Decision Analysis. Chapman and Hall, London.

Smith, J. M. and Szathmáry, E. (1999). The Origins of Life: From the Birth of Life to the Origin of Language. Oxford University Press, Oxford, UK.

Smith, R. C. and Cheeseman, P.(1986). On the representation and estimation of spatial uncertainty. International Journal of Robotics Research, 5(4), 56–68.

Smith, S. J. J., Nau, D. S., and Throop, T. A. (1998). Success in spades: Using ai planning techniques to win the world championship of computer bridge. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 1079–1086, Madison, Wisconsin. AAAI Press.

Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 2, 1–74.

Smyth, P. Heckerman, D., and Jordan, M. I. (1997). Probabilistic independence networks for hidden Markov probability models. Neural Computation, 9(2), 227–269.

Soderland, S. and Weld, D. S. (1991). Evaluating nonlinear planning. Technical report TR-91-02-03, University of Washington Department of Computer Science and Engineering, Seattle, Washington.

Solomonoff, R. J. (1964). A formal theory of inductive inference. Information and Control, 7, 1–22, 224–254.

Sondik, E. J. (1971). The Optimal Control of Partially Observable Markov Decision Processes. Ph.D. thesis, Stanford University, Stanford, California.

Sosic, r. and Gu, J. (1994). Efficient local search with conflict minimalization: A case study of the n-queens problem. IEEE Transactions on Knowledge and Data Engineering, 6(5), 661–668.

Sowa, J. (1999). Knowledge Representation: Logical, Philosophical and Computational Foundations. Blackwell, Oxford, UK.

Spiegelhalter, D. J. (1986). Probabilistic reasoning in predictive expert systems. In Kanal, L. N. and Lemmer, J. F. (Eds.), Uncertainty in Artificial Intelligence, pp. 47–67. Elsevier/North-Holland, Amsterdam, London, New York.

Spiegelhalter, D. J. Dawid, P., Lauritzen, S., and Cowell, R. (1993). Bayesian analysis in expert systems. Statistical Science, 8, 219–282.

Spielberg, S. (2001). AI. movie.

Spirtes, P., Glymour, C., and Scheines, R. (1993). Causation, prediction, and search. Springer-Verlag, Berlin.

Springsteen, B. (1992). 57 channels (and nothin’ on). In Human Touch. Sony.

Srinivasan, A., Muggleton, S. H., King, R. D., and Sternberg, M. J. E. (1994). Mutagenesis: ILP experiments in a non-determinate biological domain. In Wrobel, S. (Ed.), Proceedings of the 4th International Workshop on Inductive Logic Programming, Vol. 237, pp. 217–232. Gesellschaft für Mathematik und Datenverarbeitung MBH.

Srivas, M. and Bickford, M. (1990). Formal verification of a pipelined microprocessor. IEEE Software, 7(5), 52–64.

Stallman, R. M. and Sussman, G. J. (1977). Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9(2), 135–196.

Stanfill, C. and Waltz, D. (1986). Toward memory-based reasoning. Communications of the Association for Computing Machinery, 29(12), 1213–1228.

Stefik, M. (1995). Introduction to Knowledge Systems. Morgan Kaufmann, San Mateo, California.

Stein, L. A. (2002). Interactive Programming in Java (pre-publication draft). Morgan Kaufmann, San Mateo, California.

Steinbach, M., Karypis, G., and Kumar, V. (2000). A comparison of document clustering techniques. In KDD Workshop on Text Mining, pp. 109–110. ACM Press.

Stevens, K. A. (1981). The information content of texture gradients. Biological Cybernetics, 42, 95–105.

Stickel, M. E. (1985). Automated deduction by theory resolution. Journal of Automated Reasoning, 1(4), 333–355.

Stickel, M. E. (1988). A Prolog Technology Theorem Prover: implementation by an extended Prolog compiler. Journal of Automated Reasoning, 4, 353–380.

Stiller, L. B. (1992). KQNKRR. ICCA Journal, 15(1), 16–18.

Stillings, N. A. Weisler, S., Feinstein, M. H. Garfield, J. L., and Rissland, E. L. (1995). Cognitive Science: An Introduction (second edition). MIT Press, Cambridge, Massachusetts.

Stockman, G. (1979). A minimax algorithm better than alpha–beta?. Artificial Intelligence, 12(2), 179–196.

Stolcke, A. and Omohundro, S. (1994). Inducing probabilistic grammars by Bayesian model merging. In Proceedings of the Second International Colloquium on Grammatical Inference and Applications (ICGI-94), pp. 106–118, Alicante, Spain. Springer-Verlag.

Stone, P. (2000). Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. MIT Press, Cambridge, Massachusetts.

Strachey, C. (1952). Logical or non-mathematical programmes. In Proceedings of the Association for Computing Machinery (ACM), pp. 46–49, Toronto, Canada.

Subramanian, D. (1993). Artificial intelligence and conceptual design. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pp. 800–809, Chambery, France. Morgan Kaufmann.

Subramanian, D. and Feldman, R. (1990). The utility of EBL in recursive domain theories. In Proceedings of the Eighth National Conference an Artificial Intelligence (AAAI-90), Vol. 2, pp. 942–949, Boston, MIT Press.

Subramanian, D. and Wang, E. (1994). Constraint-based kinematic synthesis. In Proceedings of the International Conference on Qualitative Reasoning, pp. 228–239. AAAI Press.

Sugihara, K. (1984). A necessary and sufficient condition for a picture to represent a polyhedral scene. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 6(5), 578–586.

Sussman, G. J. (1975). A Computer Model of Skill Acquisition. Elsevier/North-Holland, Amsterdam, London, New York.

Sussman, G. J. and Winograd, T. (1970). MICROPLANNER Reference Manual. Ai memo 203, MIT AI Lab, Cambridge, Massachusetts.

Sutherland, I. (1963). Sketchpad: A man-machine graphical communication system. In Proceedings of the Spring Joint Computer Conference, pp. 329–346. IFIPS.

Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3, 9–44.

Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. In Solla, S. A., Leen, T. K., and Müller, K.-R. (Eds.), Advances in Neural Information Processing Systems 12, pp. 1057–1063. MIT Press, Cambridge, Massachusetts.

Sutton, R. S. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Machine Learning: Proceedings of the Seventh International Conference, pp. 216–224, Austin, Texas. Morgan Kaufmann.

Sutton, R. S. and Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press, Cambridge, Massachusetts.

Swade, D. D. (1993). Redeeming Charles Babbage’s mechanical computer. Scientific American, 268(2), 86–91.

Swerling, P. (1959). First order error propagation in a stagewise smoothing procedure for satellite observations. Journal of Astronautical Sciences, 6, 46–52.

Swift, T. and Warren, D. S. (1994). Analysis of SLG-WAM evaluation of definite programs. In Logic Programming. Proceedings of the 1994 International Symposium, pp. 219–235, Ithaca NY. MIT Press.

Syrjänen, T. (2000). Lparse 1.0 user’s manual http://saturn.tcs.hut.fi/Software/smodels.

Tadepalli, P. (1993). Learning from queries and examples with tree-structured bias. In Proceedings of the Tenth International Conference on Machine Learning, pp. 322–329, Amherst, Massachusetts. Morgan Kaufmann.

Tait, P. G. (1880). Note on the theory of the „15 puzle”. Proceedings of the Royal Society of Edinburgh, 10, 664–665.

Tamaki, H. and Sato, T. (1986). OLD resolution with tabulation. In Third International Conference on Logic Programming, pp. 84–98, London, Springer-Verlag.

Tambe, M., Newell, A., and Rosenbloom, P. S. (1990). The problem of expensive chunks and its solution by restricting expressiveness. Machine Learning, 5, 299–348.

Tarjan, R. E. (1983). Data Structures and Network Algorithms. CBMS-NSF Regional Conference Series im Applied Mathematics. SIAM (Society for Industrial and Applied Mathematics, Philadelphia.

Tarski, A. (1935). Die Wahrheitsbegriff in den formalisierten Sprachen. Studia Philosophica, 1, 261–405.

Tarski, A. (1956). Logic, Semantics, Metamathematics: Papers from 1923 to 1938. Oxford University Press, Oxford, UK.

Tash, J. K. and Russell, S. J. (1994). Control strategies for a stochastic planner. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 1079–1085, Seattle. AAAI Press.

Tate, A. (1975a). Interacting goals and their use. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence (IJCAI-75), pp. 215–218, Tbilisi, Georgia. IJCAII.

Tate, A. (1975b). Using Goal Structure to Direct Search in a Problem Solver, Ph.D. thesis, University of Edinburgh, Edinburgh, Scotland.

Tate, A. (1977). Generating project networks. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence (IJCAI-77), pp. 888–893, Cambridge, Massachusetts. IJCAII.

Tate, A. and Whiter, A. M. (1984). Planning with multiple resource constraints and an application to a naval planning problem. In Proceedings of the First Conference on AI Applications, pp. 410–416, Denver, Colorado.

Tatman, J. A. and Shachter, R. D. (1990). Dynamic programming and influence diagrams. IEEE Transactions on Systems, Man and Cybernetics, 20(2), 365–379.

Tesauro, G. (1989). Neurogammon wins computer olympiad. Neural Computation, 1(3), 321–323.

Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning, 8(3–4), 257–277.

Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the Association for Computing Machinery, 38(3), 58–68.

Tesauro, G. and Sejnowski, T. (1989). A parallel network that learns to play backgammon. Artificial Intelligence, 39(3) 357–390.

Thagard, P. (1996). Mind: Introduction to Cognitive Science. MIT Press, Cambridge, Massachusetts.

Thaler, R. (1992). The Winner’s Curse: Paradoxes and Anomalies of Economic Life. Princeton University Press, Princeton, New Jersey.

Thielscher, M. (1999). From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem. Artificial Intelligence, 111(1–2), 277–299.

Thomason, R. H. (Ed.). (1974). Formal Philosophy: Selected Papers of Richard Montague. Yale University Press, New Haven, Connecticut.

Thompson, D. W. (1917). On Growth and Form. Cambridge University Press, Cambridge, UK.

Thrun, S. (2000). Towards programming tools for robots that integrate probabilistic computation and learning. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA. IEEE.

Thrun, S. (2002). Robotic mapping: A survey. In Lakemeyer, G. and Nebel, B. (Eds.), Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann. to appear.

Titterington, D. M., Smith, A. F. M., and Makov, U. E. (1985). Statistical analysis of finite mixture dictributions. Wiley, New York.

Toffler, A. (1970). Future Shock. Bantam.

Tomasi, C. and Kanade, T. (1992). Shape and motion from image streams under orthography: A factorization method. International Journal of Computer Vision, 9, 137–154.

Touretzky, D. S. (1986). The Mathematics of Inheritance Systems. Pitman and Morgan Kaufmann, London and San Mateo, California.

Trucco, E. and Verri, A. (1998). Introductory Techniques for 3-D Computer Vision. Prentice Hall, Upper Saddle River, New Jersey.

Tsang, E. (1993). Foundations of Constraint Satisfaction. Academic Press, New York.

Tsitsiklis, J. N. and Van Roy, B. (1997). An analysis of temporal-difference learning with function approximation. IEEE Transactions on Automatic Control, 42(5), 674–690.

Tumer, K. and Wolpert, D. (2000). Collective intelligence and braess’ paradox. In Proceedings of the AAAI/IAAI, pp. 104–109.

Turcotte, M., Muggleton, S. H., and Sternberg, M. J. E. (2001). Automated discovery of structural signatures of protein fold and function. Journal of Molecular Biology, 306, 591–605.

Turing, A. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2nd series, 42, 230–265.

Turing, A. (1948). Intelligent machinery. Tech. rep., National Physical Laboratory. reprinted in (Ince, 1992).

Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

Turing, A., Strachey, C., Bates, M. A. and Bowden, B. V. (1953). Digital computers applied to games. In Bowden, B. V. (Ed.), Faster than Thought, pp. 286–310. Pitman, London.

Turtle, H. R. and Croft, W. B. (1992). A comparison of text retrieval models. The Computer Journal, 35(1), 279–289.

Tversky, A. and Kahneman, D. (1982). Causal schemata in judgements under uncrtainty. In Kahneman, D., Slovic, P., and Tversky, A. (Eds.), Judgement Under Uncertainty. Heuristics and Biases. Cambridge University Press, Cambridge, UK.

Ullman, J. D. (1985). Implementation of logical query languages for databases. ACM Transactions on Database Systems, 10(3), 289–321.

Ulmann, J. D. (1989). Principles of Database and Knowledge-Base Bystems. Computer Science Press, Rockville, Maryland.

Ullman, S. (1979). The Interpretation of Visual Motion. MIT Press, Cambridge, Massachusetts.

Vaessens, R. J. M., Aarts, E. H. I., and Lenstra, J. K. (1996). Job shop scheduling by local search. INFORMS J. on Computing, 8, 302–117.

Valiant, L. (1984). A theory of the learnable. Communications of the Association for Computing Machinery, 27, 1134–1142.

van Benthem, J. (1983). The Logic of Time. D. Reidel, Dordrecht, Netherlands.

Van Emden, M. H. and Kowalski, R. (1976). The semantics of predicate logic as a programming language. Journal of the Association for Computing Machiney, 23(4) 733–742.

van Harmelen, F. and Bundy, A. (1988). Explanation-based generalisation = partial evaluation. Artificial Intelligence, 36(3) 401–412.

van Heijenoort, J. (Ed.), (1967). From Frege to Gödel: A Source Book in Mathematical Logic, 1879–1931. Harvard University Press, Cambridge, Massachusetts.

Van Hentenryck, P., Saraswat, V., and Deville, Y. (1998). Design, implementation, and evaluation of the constraint language cc(fd). Journal of Logic Programming, 37(1–3), 139–164.

van Nunen, J. A. E. E. (1976). A set of successive approximation methods for discounted Markovian decision problems. Zeitschrift fur Operations Research, Serie A, 20(5), 203–208.

van Roy, B. (1998). Learning and value function approximation in complex decision processes. Ph.D. thesis, Laboratory for Information and Decision Systems, MIT, Cambridge, Massachusetts.

Van Roy, P. L. (1990). Can logic programming executes, as fast as imperative programming?. Report UCB/CSD 90/600, Computer Science Division, University of California, Berkeley, California.

Vapnik, V. N. (1998). Statistical Learning Theory. Wiley, New York.

Vapnik, V. N. and Chervonenkis, A. Y. (1971). On the uniform convergence of relative frequencies of relative frequencies of events to their probabilities. Theory of Probability and Its Applications, 16, 264–280.

Varian, H. R. (1995). Economic mechanism design for computerized agents. In USENIX Workshop on Electronic Commerce, pp. 13–21.

Veloso, M. and Carbonell, J. G. (1993). Derivational analogy in prodigy: Automating case acquisition, storage, and utilization. Machine Learning, 10, 249–278.

Vere, S. A. (1983). Planning in time: Windows and durations for activities and goals. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 5, 246–267.

Vinge, V. (1993). The coming technological singularity: How to survive in the post-human era. In VISION-21 Symposium. NASA Lewis Research Center and the Ohio Aerospace Institute.

Viola, P. and Jones, M. (2002). Robust real-time object detection. International Journal of Computer Vision, in press.

von Mises, R. (1928). Wahrscheinlichkeit, Statistik und Wahrheit. J. Springer, Berlin.

von Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100(295– 320).

von Neumann, J. and Morgenstern, O. (1944). Theory of Games and Economic Behavior (first edition). Princeton University Press, Princeton, New Jersey.

von Winterfeldt, D. and Edwards, W. (1986). Desicion Analysis and Behavioral Research. Chambridge University Press, Cambridge, UK.

Voorhees, E. M. (1993). Using WordNet to disambiguate word sens for text retrieval. In Sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 171–80, Pittsburgh. Association for Computing Machinery.

Vossen, T., Ball, M., Lotem, A., and Nau, D. S. (2001). Applying integer programming to ai planning. Knowledge Engieneering Review, 16, 85–100.

Waibel, A. and Lee, K.-F., (1990). Readings in Speech Recognition. Morgan Kaufmann, San Mateo, California.

Waldinger, R. (1975). Achieving several goals simultaneously. In Elcock, E. W. and Michie, D. (Eds.), Machine Intelligence 8, pp. 94–138. Ellis Horwood, Chichester, England.

Waltz, D. (1975). Understanding line drawings of scenes with shadows. In Winston, P. H. (Ed.), the Psychology of Computer Vision. McGraw-Hill, New York.

Wanner, E. (1974). On remembering, forgetting and understanding sentences. Mouton, The Hague and Paris.

Warren, D. H. D. (1974). WARPLAN: A System for Generating Plans. Department of Computational Logic Memo 76, University of Edinburgh, Edinburgh, Scotland.

Warren, D. H. D. (1976). Generating conditional plans and programs. In Proceedings of the AISB Summer Conference, pp. 344–354.

Warren, D. H. D. (1983). An abstract Prolog instruction set. Technical note 309, SRI International, Menlo Park, California.

Warren, D. H. D., Pereira, L. M., and Pereira, F. (1977). PROLOG: The language and its implementation compared with LISP. SIGPLAN Notices, 12(8), 109–115.

Watkins, C. J. (1989). Models of Delayed Reinforcement Learning. Ph.D. thesis, Psychology Department, Cambridge University, Cambridge, UK.

Watson, J. D. and Crick, F. H. C. (1953). A structure for deoxyribose nucleic acid. Nature, 171, 737.

Webber, B. L. (1983). So what can we talk about now? In Brady, M. and Berwick, R. (Eds.), Computational Models of Discourse. MIT Press, Cambridge, Massachusetts.

Webber, B. L. (1988). Tense as discourse anaphora. Computational Linguistics, 14(2), 61–73.

Webber, B. L. and Nilsson, N. J. (Eds.). (1981). Readings in Artificial Intelligence. Morgan Kaufmann, San Mateo, California.

Weidenbach, C. (2001). SPASS: Combining superposition, sorts and splitting. In Robinson, A. and Voronkov, A. (Eds.), Handbook of Automated Reasoning. mit, mit-ad.

Weiss, G. (1999). Multiagent systems. MIT Press, Cambridge, Massachusetts.

Weiss, S. and Kulikowski, C. A. (1991). Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann, San Mateo, California.

Weizenbaum, J. (1976). Computer Power and Human Reason. W. H. Freeman, New York.

Weld, D. S. (1994). An introduction to least commitment planning. AI Magazine, 15(4), 27–61.

Weld, D. S. (1999). Recent advances in ai planning. AI Magazine, 20(2) 93–122.

Weld, D. S. Anderson, C. R., and Smith, D. E. (1998). Extending graphplan to handle uncertainty and sensing actions. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 897–904, Madison, Wisconsin. AAAI Press.

Weld, D. S. and de Kleer, J. (1990). Readings in Qualitative Reasoning about Physical Systems. Morgan Kaufmann, San Mateo, California.

Weld, D. S. and Etzioni, O. 1994). The first law of robotics: A call to arms. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle. AAAI Press.

Wellman, M. P. (1985). Reasoning about preference models. Technical report MIT/LCS/TR-340, Laboratory for Computer Science, MIT, Cambridge, Massachusetts.

Wellman, M. P. (1988). Formulation of Tradeoffs in Planning under Uncertainty. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Wellman, M. P. (1990a). Fundamental concepts of qualitative probabilistic networks. Artificial Intelligence, 44(3), 257–303.

Wellman, M. P. (1990b). The STRIPS assumption for planning under uncertainty. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 198–203, Boston. MIT Press.

Wellman, M. P. (1995). The economic approach to artificial intelligence. ACM Computing Surveys, 27(3), 360–362.

Wellman, M. P., Breese, J. S., and Goldman, R. (1992). From knowledge bases to decision models. Knowledge Engieneering Review, 7(1), 35–53.

Wellman, M. P. and Doyle, J. (1992). Modular utility representation for decision-theoretic planning. In Proceedings, First International Conference on AI Planning Systems, pp. 236–242, College Park, Maryland. Morgan Kaufmann.

Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Ph.D. thesis, Harvard University, Cambridge, Massachusetts.

Werbos, P. (1977). Advanced forecasting methods for global crisis warning and models of intelligence. General Systems Yearbook, 22, 25–38.

Wesley, M. A. and Lozano-Perez, T. (1979). An algorithm for planning collision-free paths among polyhedral objects. Communications of the ACM, 22(10), 560–570.

Wheatstone, C. (1838). On some remarkable, and hitherto unresolved, phenomena of binocular vision. Philosophical Transactions of the Royal Society of London, 2, 371–394.

Whitehead, A. N. (1911). An Introduction to Mathematics. Williams and Northgate, London.

Whitehead, A. N. and Russell, B. (1910). Principia Mathematica. Cambridge University Press, Cambridge, UK.

Whorf, B. (1956). Language, Thought, and Reality. MIT Press. Cambridge, Massachusetts.

Widrow, B. (1962). Generalization and information storage in networks of adaline „neurons”. In Yovits, M. C. Jacobi, G. T., and Goldstein, G. D. (Eds.), Self-Organizing Systems 1962, pp. 435–461, Chicago, Illionis. Spartan.

Widrow, B. and Hoff, M. E. (1960). Adaptive switching circuits. In 1960 IRE WESCON Convention Record, pp. 96–104, New York.

Wiener, N. (1942). The extrapolation, interpolation, and smoothing of stationary time series. Osrd 370, Report to the Services 19, Research Project DIC-6037, MIT, Cambridge, Massachusetts.

Wiener, N. (1948). Cybernetics. Wiley, New York.

Wilensky, R. (1978). Understanding goal-based stories. Ph.D. thesis, Yale University, New Haven, Conecticut.

Wilensky, R. (1983). Planning and Understanding. Addison-Wesley, Reading, Massachusetts.

Wilkins, D. E. (1980). Using patterns and plans in chess. Artificial Intelligence, 14(2), 165–203.

Wilkins, D. E. (1988). Practical Planning: Extending the AI Planning Paradigm. Morgan Kaufmann, San Mateo, California.

Wilkins, D. E. (1990). Can AI planners solve practical problems?. Computational Intelligence, 6(4), 232–246.

Wilkins, D. E., Myers, K. L., Lowrance, J. D., and Wesley, L. P. (1995). Planning and reacting in uncertain and dynamic environments. Journal of Exprimental and Theoretical AI, 7(1), 197–227.

Wilks, Y. (1975). An intelligent analyzer and understanding of English. Communications of the ACM, 18(5), 264–274.

Williams, R. J.(1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229–256.

Williams, R. J. and Baird, L. C. I. (1993). Tight performance bounds on greedy policies based on imperfect value functions. Tech. rep. NU-CCS-93-14, College of Computer Science, Northeastern University, Boston.

Wilson, R. A. and Keil, F. C. (Eds.). (1999). The MIT Encyclopedia of the Cognitive Sciences. MIT Press, Cambridge, Massachusetts.

Winograd, S. and Cowan, J. D. (1963). Reliable Computation in the Presence of Noise. MIT Press, Cambridge, Massachusetts.

Winograd, T. (1972). Understanding natural language. Cognitive Psychology, 3(1), 1–191.

Winston, P. H. (1970). Learning structural descriptions from examples. Technical report MAC-TR-76, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Winston, P. H. (1992). Artificial Intelligence (Third edition). Addison-Wesley, Reading, Massachusetts.

Wirth, R. and O’Rorke, P. (1991). Constraints on predicate invention. In Machine Learning: Proceedings of the Eighth International Workshop (ML-91), pp. 457–461, Evanston, Illionis. Morgan Kaufmann.

Witten, I. H. and Bell, T. C. (1991). The zero frequency problem: Estimating the probabilistic of novel events in adaptive text compression. IEEE Transactions on Information Theory, 37(4), 1085–1094.

Witten, I. H., Moffat, A., and Bell, T. C. (1999). Managing Gigabytes: Compressing and Indexing Documents and Images (second edition). Morgan Kaufmann, San Mateo, California.

Wittgenstein, L. (1922). Tractatus Logico-Philosophicus (second edition). Routledge and Kegan Paul, London. Reprinted 1971, edited by D. F. Pears and B. F. McGuinness. This edition of the English translation also contains Wittgenstein’s original German text on facing pages, as well as Bertrand Russell’s introduction to the 1922 edition.

Wittgenstein, L. (1953). Philosophical Investigations. Macmillan, London.

Wojciechowski, W. S. and Wojcik, A. S. (1983). Automated design of multiple-valued logic circuits by automated theorem proving techniques. IEEE Transactions on Computers, C-32(9), 785– 798.

Wojcik, A. S. (1983). Formal design verification of digital systems. In ACM IEEE 20th Design Automation Conference Proceedings, pp. 228– 234, Miami Beach, Florida. IEEE.

Wood, M. K. and Dantzig, G. B. (1949). Programming of interdependent activities. i. general discussion. Econometrica, 17, 193–199.

Woods, W. A. (1973). Progress in natural language understanding: An application to lunar geology. In AFIPS Conference Proceedings, Vol. 42, pp. 441–450.

Woods, W. A. (1975). What’s in a link? Foundations for semantic networks. In Bobrow, D. G. and Collins, A. M. (Eds.), Representation and Understanding: Studies in Cognitive Science, pp. 35–82. Academic Press, New York.

Woods, W. A. (1978). Semantics and quantification in natural language question answering. In Advances in Computers. Academic Press.

Wooldridge, M. and Rao, A. (Eds.). (1999). Foundations of national agency. Kluwer, Dordrecht, Netherlands.

Wos, L., Carson, D., and Robinson, G. (1964). The unit preference strategy in theorem proving. In Proceedings of the Fall Joint Computer Conference, pp. 615–621.

Wos, L., Carson, D., and Robinson, G. (1965). Efficiency and completeness of the set-of-support strategy in theorem proving. Journal of the Association for Computing Machinery, 12, 536–541.

Wos, L., Overbeek, R., Lusk, E., and Boyle, J. (1992). Automated Reasoning: Introduction and Applications (second edition). McGraw-Hill, New York.

Wos, L. and Robinson, G. (1968). Paramodulation and set of support. In Proceedings of the IRIA Symposium on Automatic Demonstration, pp. 276–310. Springer-Verlag.

Wos, L., Robinson, G., Carson, D., and Shalla, L. (1967). The concept of demodulation in theorem proving. Journal of the Association for Computing Machinery, 14, 698–704.

Wos, L. and Winker, S. (1983). Open questions solved with the assistance of AURA. In Bledsoe, W. W. and Loveland, D. (Eds.), Automated Theorem Proving: After 25 Years: Proceedings of the Special Session of the 89th Annual Meeting of the American Mathematical Society, pp. 71–88, Denver, Colorado. American Mathematical Society.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.

Wright, S. (1931). Evolution in Mendelian populations. Genetics, 16, 97–159.

Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5, 161–215.

Wu, D. (1993). Estimating probability distributions over hypotheses with variable unification. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pp. 790–795, Chambery, France. Morgan Kaufmann.

Wygant, R. M. (1989). CLIPS – a powerful development and delivery expert system tool. Computers and Industrial Engieneering, 17, 546–549.

Yamada, K. and Knight, K. (2001). A syntax-based statistical transaction model. In Proceedings of the Thirty Ninth Annual Conference of the Association for Computational Linguistics, pp. 228–235.

Yang, Q. (1990). Formalizing planning knowledge for hierarchical planning. Computational Intelligence, 6, 12–24.

Yang, Q. (1997). Intelligent planning: A decomposition and abstraction based approach. Springer-Verlag, Berlin.

Yedidia, J., Freeman, W., and Weiss, Y. (2001). Generalized belief propagation. In Leen, T. K. Dietterich, T., and Tresp, V. (Eds.), Advances in Neural Information Processing Systems 13. MIT Press, Cambridge, Massachusetts.

Yip, K. M.-K. (1991). KAM: A System for Intelligently Guiding Numerical Experimentation by Computer. MIT Press, Cambridge, Massachusetts.

Yngve, V. (1955). A model and an hypothesis for language structure. In Locke, W. N. and Booth, A. D. (Eds.), Machine Translation of Languages, pp. 208–226. MIT Press, Cambridge, Massachusetts.

Yob, G. (1975). Hunt the wumpus!. Creative Computing, Sep/Oct.

Yoshikawa, T. (1990). Foundations of Robotics: Analysis and Control. MIT Press, Cambridge, Massachusetts.

Young, R. M. Pollack, M. E., and Moore, J. D. (1994). Decomposition and causality in partial order planning. In Proceedings of the 2nd International Conference on Artificial Intelligence Planning Systems (AIPS-94), pp. 188–193, Chicago.

Younger, D. H. (1967). Recognition and parsing of context-free languages in time n³. Information and Control, 10(2), 189–208.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3–28.

Zaritskii, V. S., Svetnik, V. B. and Shimelevich, L. I. (1975). Monte-Carlo technique in problems of optimal information processing. Automation and Remote Control, 36, 2015–22.

Zelle, J. and Mooney, R. J. (1996). Learning to parse database queries using inductive logic programming. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 1050– 1055.

Zermelo, E. (1913). Uber Eine Anwendung der Mengenlehre auf die Theorie des Schachspiels. In Proceedings of the Fifth International Congress of Mathematicians, Vol. 2, pp. 501–504.

Zermelo, E. (1976). An application of set theory to the theory of chess-playing. Firbush News, 6, 37–42. English translation of (Zermelo 1913).

Zhang, N. L. and Poole, D. (1994). A simple approach to bayesian network computations. In Proceedings of the 10th Canadian Conference on Artificial Intelligence, pp. 171–178, Banff, Alberta, Morgan Kaufmann.

Zhang, N. L. and Poole, D. (1996). Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research, 5, 301–328.

Zhou, R. and Hansen, E. (2002). Memory-bounded A* graph search. In Proceedings of the 15th International Flairs Conference.

Zhu, D. J. and Latombe, J.-C. (1991). New heuristic algorithms for efficient hierarchical path planning. IEEE Transaction on Robotics and Automation, 7(1), 9–20.

Zilberstein, S. and Russell, S. J. (1996). Optimal composition of real-time systems. Artificial Intelligence, 83, 181–213.

Zimmermann, H.-J. (Ed.). (1999). Practical application of fuzzy technologies. Kluwer, Dordrecht, Netherlands.

Zimmermann, H.-J. (2001). Fuzzy Set Theory – And Its Applications (Fourth edition). Kluwer, Dordrecht, Netherlands.

Zobrist, A. L. (1970). Feature Extraction and Representation for Pattern Recognition and the Game of Go. Ph.D. thesis, University of Wisconsin.

Zuse, K. (1945). The Plankalkül. Report 175, Gesellschaft für Mathematik und Datenverarbeitung, Bonn, Germany.

Zweig, G. and Russell, S. J. (1998). Speech recognition with dynamic Bayesian networks. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), pp. 173–180, Madison, Wisconsin. AAAI Press.