Primary Text

The primary text for the course is: Artificial Intelligence: A Modern Approach, 2nd Edition, by Stuart Russell and Peter Norvig.

The course will draw on several additional texts and references.

Artificial Intelligence

1. Dean, T., Allen, J. & Aloimonos, Y., Artificial Intelligence theory and practice. New York: Benjamin Cummings (1995).
2. Ginsberg, M., Essentials of Artificial Intelligence. Palo Alto, CA: Morgan Kaufmann (1993).
3. Luger, G. F., & Stubblefield, W. A., Artificial Intelligence - Structures and Strategies for Complex Problem Solving. New York, NY: Addison Wesley, 5th edition (2005).
4. Poole, D., Mackworth, A., and Goebel, R. Computational Intelligence - A Logical Approach. New York: Oxford University Press. (1998).
5. Nilsson, N. J. Artificial Intelligence - A Modern Synthesis. Palo Alto: Morgan Kaufmann. (1998).
6. Nilsson, N. J., Principles of Artificial Intelligence. Palo Alto, CA: Tioga (1981).
7. Rich, E., & Knight, K., Artificial Intelligence. New York: McGraw-Hill (1991).
8. Russell, S. & Norvig, P., Artificial Intelligence - a modern approach. 2nd Edition. Englewood Cliffs, NJ: Prentice Hall (2002).
9. Tanimoto, S., The Elements of Artificial Intelligence Using Common Lisp. 2nd Edition. New York, NY: Computer Science Press (1995).

Knowledge Representation and Inference

1. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., & Patel-Schneider, P. The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press (2003).
2. Brachman, R. J. & Levesque, H. J. Knowledge Representation. New York: Elsevier (2004).
3. Castillo, E., Gutierrez, J. M., Hadi, A. S. Expert Systems and Probabilistic Network Models. Berlin: Springer (1996).
4. Cowell, R. G. Lauritzen, S. L., and Spiegelhalter, D. J. Probabilistic Networks and Expert Systems Berlin: Springer (2005).
5. Davis, E. Representations of Commonsense Knowledge. Palo Alto, CA: Morgan Kaufmann (1990).
6. Dechter, R. Constraint Processing. Palo Alto, CA: Morgan Kaufmann. (2003).
7. Forbus, K. & De Kleer, J., Building Problem Solvers, Cambridge, MA: MIT Press (1993).
8. Genesereth, M. R., & Nilsson, N. J., Logical Foundations of Artificial Intelligence. Palo Alto, CA: Morgan Kaufmann (1987).
9. Gomez-Perez, A., Corcho, O., & Fernandez-Lopez, M. Ontological Engineering. Berlin: Springer (2004).
10. Jensen, F. Bayesian Networks and Decision Graphs. Berlin: Springer (2002).
11. Newborn, M. Automated Theorem Proving: Theory and Practice. Berlin: Springer (2000).
12. Pearl, J. Probabilistic Reasoning in Intelligent Systems. Palo Alto, CA: Morgan Kaufmann (1986)
13. Pearl, J. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press (2000).
14. Sowa, J. F. Knowledge Representation: Logical, Philosophical, and Computational Foundations, Pacific Grove, CA: Brooks Cole. (2000).

Decision Making

1. Bather, J. Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions. New York: Wiley (2000).
2. French, S. Decision Theory - An Introduction to the Mathematics of Rationality, Mathematics and Its Applications. 1988.
3. Luce, D. & Raiffa, H. Games and Decisions: Introduction and Critical Survey. Dover Reprint (1989).
4. Puterman, M. L. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: Wiley. (2005).

Learning

1. Bishop, C. M. Neural Networks for Pattern Recognition. New York: Oxford University Press (1995).
2. Bishop, C. M. Machine Learning and Pattern Recognition. Berlin: Springer (2006).
3. Cristianini, N. & Shawe-Taylor, J. An Introduction to Support Vector Machines. London: Cambridge University Press. (2000).
4. Duda, R., Hart, P., & Stork, D. Pattern Classification. New York: Wiley. (2001).
5. Hastie, T., Tibshirani, R., & Friedman, J. The elements of Statistical Learning - Data Mining, Inference, and Prediction. Berlin: Springer-Verlag. (2001).
6. Kearns, M. & Vazirani, U. Computational Learning Theory. Cambridge, MA: MIT Press. (1994).
7. Mitchell, T. Machine Learning. New York: Mc Graw-Hill. (1997).
8. Neapolitan, R. E. Learning Bayesian Networks. New York: Prentice Hall. (2003).
9. Sutton, R. S. & Barto, A. G. Reinforcement Learning. Cambridge, MA: MIT Press (1998).
10. Ripley, B. D. Pattern Recognition and Neural Networks. New York: Cambridge University Press. (1996).
11. Tan, P-N., Steinbach, M., & Kumar, V. Introduction to Data Mining. New York: Addison-Wesley (2004).
12. Uhr, L. Pattern Recognition, Learning, and Thought. New York: Prentice Hall (1973).

Planning

1. Ghallab, M., Nau, D., & Traverso, P. Automated Planning : Theory & Practice. Palo Alto: Morgan Kaufmann (2005).
2. Yang, Q. Intelligent Planning: A Decomposition and Abstraction Based Approach. Berlin: Springer (1998).

Perception

1. Fischler, M., & Firschein, O., Intelligence -- The Eye, the Brain, and the Computer. New York: Addison-Wesley (1987).
2. Forsyth, D. A., and Ponce, J. Computer Vision: A Modern Approach. New York: Prentice-Hall (2002).
3. Davies, E. R. Machine Vision : Theory, Algorithms, Practicalities. Palo Alto: Morgan Kaufmann (2004).
4. Jain, R., Kasturi, R., & Schunck, B. G. Machine Vision. New York: McGraw-Hill (1995).
5. Snyder, W. E. and Qi, H. Machine Vision. London: Cambridge University Press. (2004).

Speech and Language Processing

1. Baeza-Yates & Rebeiro-Neto. Modern Information Retrieval. New York: Addison-Wesley. (1999).
2. Berry, M. W., & Browne, M. Understanding Search Engines: Mathematical Modeling and Text Retrieval. SIAM, (1999).
3. Charniak, E. Statistical Language Learning. Cambridge, MA: MIT Press (1996).
4. Chakrabarti, S. Mining the Web: Analysis of Hypertext and Semi Structured Data. Palo Alto: Morgan Kaufmann (2002).
5. Jelinek, F. Statistical Methods for Speech Recognition. Cambridge, MA: MIT Press (1998).
6. Jurafsky, M. & Martin, J. Speech and Language Processing. New York: Prentice-Hall (2000).
7. Manning, C. & Schutze, H. Foundations of Statistical Natural Language Processing, Cambridge, MA: MIT Press (1999).
8. Grossman, D.A. & Frieder, O. Information Retrieval: Algorithms and Heuristics. Berlin: Springer (2004).
9. Salton, G. & McGill, M. J. Introduction to Modern Information Retrieval. McGraw-Hill (1983).

Robotics

1. Arkin, A. Behavior-Based Robotics. Cambridge, MA: MIT Press (1998).
2. Braitenberg, V. Vehicles: Experiments in Synthetic Psychology. Cambridge, MA: MIT Press (1986).
3. Dudek, G., and Jenkin, M. Computational Principles of Mobile Robotics. Cambridge University Press (2000).
4. Murphy, R. An Introduction to AI Robotics. Cambridge, MA: MIT Press (2000).
5. Siegwart, R. and Nourbakhsh, I. R. Introduction to Autonomous Mobile Robots Cambridge, MA: MIT Press (2004).
6. Thrun, S., Burgard, W. & Fox, D. Probabilistic Robotics. Cambridge, MA: MIT Press (2005).

Multi-Agent Systems

1. Ferber, J. Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence New York: Addison Wesley. (1999).
2. Minsky, M. Society of Mind. New York: Basic Books (1986).
3. Weiss, G. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA: MIT Press (2000).
4. Woolridge, M. Introduction to MultiAgent Systems. New York: Wiley (2002).

Artificial Intelligence Programming

Java Books

1. Schildt, H. Java 2: A Beginner's Guide. McGraw-Hill (2003).
2. Sierra, K. and Bates, B. Head First Java. O'Reilley (2003).
3. Sikora, M. Java: A Practical Guide for Programmers. Morgan Kaufmann (2002).

Lisp Books

1. Forbus, K. D. & de Kleer, J. Building Problem Solvers. Cambridge, MA: MIT Press. (1993).
2. Graham, P. ANSI Common Lisp. Englewood Cliffs, NJ: Prentice Hall (1995).
Graham, P. On Lisp (downloadable). Prentice Hall (1993).
3. Norvig, P., Paradigms of Artificial Intelligence Programming -- Case Studies in Common Lisp. Palo Alto, CA: Morgan Kaufmann (1992).
4. Siebel, P. Practical Common LISP (downloadable). Apress. (2005).
5. Queinnec, C. Lisp in Small Pieces. Cambridge University Press (2003).

Prolog Books

1. Blackburn, P., Bos, J. and Striegnitz, K. Learn PROLOG Now! College Publications (2006).
2. Bramer, M. Logic Programming with Prolog. Springer (2005).
3. Bratko, I. Prolog Programming for Artificial Intelligence. Addison Wesley. (2000).
4. Clockskin, W. and Mellish, C. Programming in PROLOG. Springer (2003).
5. Clocksin, W. Clause and Effect: PROLOG Programming for the Working Programmer. Springer (1997).

Philosophy of Mind and Philosophy of AI

1. Audi, R. Epistemology: A Contemporary Introduction. Routledge (2003).
2. Barkow, J. H., Cosmides, L. & Tooby, J. The Adapted Mind. New York: Oxford Univ. Press (1992).
3. Bickerton, D. Language Species. University of Chicago Press (1992)
4. Bringsjord, S. What Robots Can and Can't Be. Kluwer (1992).
5. Boden, M. A., The Creative Mind. New York, NY: Basic Books (1990).
6. Calvin, W. H. The Cerebral Code. New York: Bantam Books (1990).
7. Calvin, W. H. How Brains Think: Evolving Intelligence, Then and Now. New York: Basic Books (1997).
8. Chomsky, N. Language and the Mind. 3rd Edition. Cambridge University Press (2006).
9. Churchland, P. Neurophilosophy: Toward a Unified Science of the Mind-Brain. Cambridge, MA: MIT Press (1989).
10. Churchland, P. Brain-Wise: Studies in Neurophilosophy. Cambridge, MA: MIT Press (2002).
11. Copeland, J. Artificial Intelligence: A Philosophical Introduction. Blackwell (1993).
12. Copeland, J. (ed). The Essential Turing. Oxford University Press (2004).
13. Damasio, A. R. Descartes' Error -- Emotion, Reason, and the Human Brain. New York: G. P. Putnam's Sons. (1994)
14. Deacon, T. The Symbolic Species. New York: W. W. Norton. (1998).
15. Deledalle, G. Charles Peirce's Philosophy of Signs. Indiana University Press (2000).
16. Dennett, D.C. Kinds of Minds. New York: Basic Books (1996).
17. Dennett, D.C. Darvin's Dangerous Idea. New York: Simon and Schuster (1995).
18. Dretske, F. I. Knowledge and the Flow of Information. CSLI Press, Stanford University (1999).
19. Donald, M. D. Origins of the Modern Mind. Cambridge, Mass: Harvard Univ. Press. (1992).
20. Dreyfus, H. L., What Computers Can't Do. New York, NY: Harper & Row (1979).
21. Eco, U. Theory of Semiotics. Indiana University Press. (1979).
22. Emmeche, C. The Garden in the Machine: The Emerging Science of Artificial Life. Princeton, NJ: Princeton University Press (1994).
23. Franklin, S. Artificial Minds. Cambridge, MA: MIT Press. (1995).
24. Haugeland, J., Artificial Intelligence - The Very Idea. Boston, MA:MIT Press (1985).
25. Hawkins, J. On Intelligence. Times Books (2004).
26. Heil, J. Philosophy of Mind. London: Routledge (2004).
27. Holland, J. Origins of Order. New York, NY: Addison Wesley (1995).
28. Kim, J. Philosophy of Mind. Westview Press (2005).
29. Kurzweil, R. The Singularity Is Near: When Humans Transcend Biology. New York: Viking Books (2005)
30. Lycan, W. Philosophy of Language: A Contemporary Introduction Routedge (1999).
31. Maturana, H.R. & Varela, F.J. The Tree of Knowledge. Boston: Shambala (1992).
32. McDowell, J. Mind and World. Harvard University Press (1996).
33. Minsky, M. Society of Mind. New York: Basic Books (1986).
34. Moravec, H., Mind Children: The Future of Robot and Human Intelligence. Cambridge, MA: Harvard University Press (1988).
35. Pinker, S. The Language Instinct. New York: Pengin (1994)
36. Quine, W. V. O. Ontological Relativity and Other Essays. Columbia University press. 1977.
37. Quine, W. V. O. Quintessence. Basic Readings from the Philosophy of W. V. Quine. Belknap Press (2004).
38. Robinson, W. S. Computers, Minds, and Robots. Philadephia, PA: Temple University Press (1992).
39. Searle, J. Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press (1969).
40. Searle, J. Mind, Language, and Society : Philosophy in the Real World. Basic Books (2000).
41. Simon, H. A., Sciences of the Artificial. Cambridge, MA: MIT Press (1981).
42. Skinner, B. F. Science and Human Behavior. Free Press (1965).
43. Skinner, B. F. About Behaviorism. Vintage (1976).
44. Varela, F.J., Thompson, E., & Rosch, E. The Embodied Mind. Cambridge: MIT Press. (1992).
45. Von Neumann, J. Computer and the Brain. 2nd Edition. Yale University Press (2000).