Pennsylvania State University

Pennsylvania State University

Principles of Artificial Intelligence

Primary Text

The primary text for the course is: Artificial Intelligence: A Modern Approach, 3rd Edition, by Stuart Russell and Peter Norvig.

Course textbook

The course will draw on several additional texts and references.

Artificial Intelligence

  1. Hawkins, J. and Blakeslee, S. On Intelligence. Times Books, 2004.
  2. Dean, T., Allen, J. & Aloimonos, Y., Artificial Intelligence theory and practice. New York: Benjamin Cummings (1995).
  3. Ginsberg, M., Essentials of Artificial Intelligence. Palo Alto, CA: Morgan Kaufmann (1993).
  4. Luger, G. F., Artificial Intelligence - Structures and Strategies for Complex Problem Solving. New York, NY: Addison Wesley, 6th edition (2008).
  5. Poole, D., Mackworth, A. Artificial Intelligence - Foundations of Computational Agents. New York: Cambridge University Press. 2nd Edition (2017).
  6. Nilsson, N. J. Artificial Intelligence - A Modern Synthesis. Palo Alto: Morgan Kaufmann. (1998).
  7. Nilsson, N. J., Principles of Artificial Intelligence. Palo Alto, CA: Tioga (1981).
  8. Rich, E., Knight, K., & Nair, S. Artificial Intelligence. 3rd Edition. New York: McGraw-Hill (2010).
  9. Tanimoto, S., The Elements of Artificial Intelligence Using Common Lisp. 2nd Edition. New York, NY: Computer Science Press (1995).
  10. Winston, P.H., Artificial Intelligence. 3rd Edition. New York, NY: Addison Wesley.

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. Darwiche, A. Modeling and Reasoning with Bayesian Networks. New York, NY: Cambridge University Press (2014).
  7. Dechter, R. Constraint Processing. Palo Alto, CA: Morgan Kaufmann. (2003).
    8. Fagin, R., Halpern, J.Y., Moses, Y., & Vardi, M. Reasoning about knowledge. Cambridge, MA: MIT Press. (1995).
  8. Forbus, K. & De Kleer, J., Building Problem Solvers, Cambridge, MA: MIT Press (1993).
  9. Gasquet, O., Herzig, A., Said, B. & Schwartzentruber, F. Kripke's Worlds. An Introduction to Modal Logics via Tableaux. Berlin: Birkhauser (2014).
  10. Genesereth, M. R., & Nilsson, N. J., Logical Foundations of Artificial Intelligence. Palo Alto, CA: Morgan Kaufmann (1987).
  11. Gomez-Perez, A., Corcho, O., & Fernandez-Lopez, M. Ontological Engineering. Berlin: Springer (2004).
  12. Hitzler, P., Krotzsch, M., and Rudolph, S. Foundations of Semantic Web Technologies. Chapman and Hall. (2010).
  13. Klein, P. Coding the Matrix. Linear Algebra through Computer Science Applications. Newtonian Press. 2013.
  14. Koller, D. & Friedman, D. Probabilistic Graphical Models. Cambridge, MA: MIT Press (2009).
  15. Korb, K. & Nicholson, A. Bayesian Artificial Intelligence. New York, NY: Chapman & Hall/CRC (2003).
  16. Koski, T. & Noble, J.M. Bayesian Networks. New York: Wiley. (2009).
  17. Jensen, F. Bayesian Networks and Decision Graphs. Berlin: Springer (2002).
  18. Meyer, J-J. Ch. & van der Hoek, W. Epistemic Logic for AI and Computer Science, New York, NY: Cambridge University Press (2004).
  19. Newborn, M. Automated Theorem Proving: Theory and Practice. Berlin: Springer (2000).
  20. Pearl, J. Probabilistic Reasoning in Intelligent Systems. Palo Alto, CA: Morgan Kaufmann (1986)
  21. Pearl, J. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press (2000).
  22. Santhanam, G., Basu, S., & Honavar, V. Representing and Reasoning with Qualitative Preferences. Morgan Claypool. 2016.
  23. Sowa, J. F. Knowledge Representation: Logical, Philosophical, and Computational Foundations, Pacific Grove, CA: Brooks Cole. (2000).
  24. Strang, G. Introduction to Linear Algebra. Fifth Edition. Wellesley-Cambridge Press. 2016.

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. Abu-Mostafa, Y., Magdon-Ismail, M., and Lin, H-T. (2012). Learning from Data. AMLBook.com
  2. Agrawal, D. & Chen, B-C. Statistical Methods for Recommender Systems. New York, NY: Cambridge University Press. (2016).
  3. Baldi, P., Frasconi, P., Smyth, P. Modeling the Internet and the Web - Probabilistic Methods and Algorithms. New York: Wiley. (2003).
  4. Barber, D. Bayesian Reasoning and Machine Learning. Cambridge University Press. (2012)
  5. Bishop, C. M. Neural Networks for Pattern Recognition. New York: Oxford University Press (1995).
  6. Buhlmann, P. & van de Geer, S. Statistics for High-Dimensional Data Analysis. Berlin; Springer (2012).
  7. Chakrabarti, S. Mining the Web, Morgan Kaufmann. (2003).
  8. Cowell, R.G., Dawid, A.P., Lauritzen, S.L., and Spiegelhalter,D.J. Graphical Models and Expert Systems.Berlin: Springer. (1999)
  9. Cristianini, N. and Shawe-Taylor, J. An Introduction to Support Vector Machines. London: Cambridge University Press. (2000).
  10. Devroye, Luc, Györfi, László, Lugosi, Gabor. A probabilistic theory of pattern recognition. Springer. (1996).
  11. Duda, R., Hart, P., and Stork, D. Pattern Classification. New York: Wiley. (2001).
  12. Goodfellow, I. & Benjio, Y. Deep Learning. Cambridge, MA: MIT Press. (2017).
  13. Hastie, T., Tibshirani, R., and Friedman, J. The elements of Statistical Learning - Data Mining, Inference, and Prediction. Berlin: Springer-Verlag. (2011)
  14. Hastie, T., Tibshirani, R., and Wainwright, M. Statistical Learning with Sparsity. CRC Press. (2015).
  15. Leskovec, U., Rajaraman, A., and Ullman, J. Mining Massive Data Sets (2014)
  16. Kearns, M. & Vazirani, U. Computational Learning Theory. Cambridge, MA: MIT Press. (1994).
  17. Koller, D. & Friedman, N. Probabilistic Graphical Models. MIT Press. (2009)
  18. Mitchell, T. Machine Learning. New York: Mc Graw-Hill. (1997).
  19. Mohri, M., Rostamzadehm A., and Talwalker, A. (2012). Foundations of Machine Learning MIT Press, 2012.
  20. Murphy, K. Machine Learning: A probabilistic perspective. MIT Press. (2012)
  21. Natarjan, B. Machine Learning: A Theoretical Approach. Kluwer. (2001)
  22. Neapolitan, R. Learning Bayesian Networks. Prentice-Hall. (2004).
  23. Skolkopf, B. & Smola, A. Learning with Kernels. MIT Press. (2001)
  24. Sutton, R. S. & Barto, A. G. Reinforcement Learning. Cambridge, MA: MIT Press (1998).
  25. Tan, P-N., Steinbach, M., & Kumar, V. Introduction to Data Mining. New York: Addison-Vesley. (2004).
  26. Theodoridis, S. Machine Learning. Springer. (2015)
  27. Uhr, L. Pattern Recognition, Learning, and Thought. New York: Prentice Hall (1973). Vapnik, V. (1998). Statistical Learning Theory. Wiley.
  28. Vidyasagar, M. (2002). A theory of learning and generalization, with applications to Neural Networks. Springer.
  29. Watt, J. and Borhani, R. Machine Learning Refined. New York, NY: Cambridge University Press. (2016).


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 (2014).
  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. Prince S.J.D. Computer Vision: Models, Learning, and Inference. (2012)
    6. Shapiro, L.G. & Stockman, G.C. Computer Vision. Pearson. (2001).
  6. Snyder, W. E. and Qi, H. Machine Vision. London: Cambridge University Press. (2004).
    7. Szeliski, R. Computer Vision. Berlin: Springer (2011).

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).
    3. 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).