University of Massachusetts Amherst
Department of Computer Science




Reasoning and Acting under Uncertainty

Fall 2004


Shlomo Zilberstein



  1. K. Arrow and H. Raynaud. Social Choice and Multicriterion Decision-Making, MIT Press, 1986.

  2. F. Bacchus. Representing and Reasoning with Probabilistic Knowledge: A Logical Approach to Probabilities, MIT Press, 1990.

  3. D.S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein. The Complexity of Decentralized Control of Markov Decision Processes, Mathematics of Operations Research, to appear.

  4. Craig Boutilier, Thomas Dean, and Steve Hanks. Decision-Theoretic Planning: Structural Assumptions and Computational Leverage, Journal of Artificial Intelligence Research, 11:1-94, 1999.

  5. Wray Buntine. A Guide to the Literature on Learning Probabilistic Networks From Data, IEEE Trans. On Knowledge And Data Engineering, 8:195-210, 1996.

  6. Enrique Castillo, Jose Gutierrez, and Ali Hadi. Expert Systems and Probabilistic Network Models, Springer, 1997.

  7. Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, and David J. Spiegelhalter. Probabilistic Networks and Expert Systems, Springer, 1999.

  8. Rina Dechter. Bucket Elimination: A Unifying Framework for Reasoning, Artificial Intelligence, 113(1-2):41-85, 1999.

  9. Jon Doyle and Richmond H. Thomason. Background to Qualitative Decision Theory, AI Magazine, 20(2):55-68, 1999.

  10. Didier Dubois and Henri Prade. A Survey of Belief Revision and Updating Rules in Various Uncertainty Models, International Journal of Intelligent Systems, 9(1):61-100, 1994.

  11. Zhengzhu Feng and Eric A. Hansen. Symbolic Heuristic Search for Factored Markov Decision Processes, Proceedings of the Eighteenth National Conference on Artificial Intelligence, 455-460, 2002.

  12. Zhengzhu Feng and Shlomo Zilberstein. Region-Based Incremental Pruning for POMDPs., Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, 2004.

  13. D. Geiger and J. Pearl. On the Logic of Causal Models. In R.D. Shachter et al., editors, Uncertainty in Artificial Intelligence 43-14, 1990.

  14. J.C. Giarratano and G.D. Riley. Expert Systems: Principles and Programming. Thomson Course Technology, 2005.

  15. Claudia V. Goldman and Shlomo Zilberstein. Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis., Journal of Artificial Intelligence Research, to appear.

  16. Joseph Y. Halpern. A Logical Approach to Reasoning about Uncertainty: A Tutorial, In Discourse, Interaction, and Communication, X. Arrazola, K. Korta, and F.J. Pelletier, (eds.), Kluwer, 1997.

  17. Joseph Y. Halpern and Judea Pearl. Causes and explanations: A structural-model approach ---Part I: Causes, Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, 194-202, 2001.

  18. Joseph Y. Halpern and Judea Pearl. Causes and explanations: A structural-model approach ---Part II: Explanations, Proceedings of the 16th International Joint Conference on Artificial Intelligence, 2001.

  19. Eric A. Hansen, Daniel S. Bernstein, and Shlomo Zilberstein. Dynamic Programming for Partially Observable Stochastic Games, Proceedings of the Nineteenth National Conference on Artificial Intelligence, 2004.

  20. Eric Hansen and Shlomo Zilberstein. Monitoring and Control of Anytime Algorithms: A Dynamic Programming Approach, Artificial Intelligence, 126(1-2):139-158, 2001.

  21. Eric Hansen and Shlomo Zilberstein. LAO*: A Heuristic Search Algorithm that Finds Solutions with Loops, Artificial Intelligence, 129(1-2):35-62, 2001.

  22. David Heckerman. A Tutorial on Learning with Bayesian Networks, Technical Report MSR-TR-95-06, Microsoft Research, Redmond, Washington, 1995. (Revised June 1996.)

  23. David Heckerman and John S. Breese. Causal Independence for Probability Assessment and Inference Using Bayesian Networks, IEEE Systems, Man, and Cybernetics, 26:826-831, 1996.

  24. Jesse Hoey, Robert St-Aubin, Alan Hu, and Craig Boutilier. Optimal and Approximate Stochastic Planning using Decision Diagrams University of British Columbia Technical Report TR-00-05, June 2000.

  25. Ronald Howard. Information Value Theory, IEEE Transactions on System Science and Cybernetics, SSC-2(1):22-26, 1966.

  26. Cecil Huang and Adnan Darwiche. Inference in Belief Networks: A Procedural Guide, International Journal of Approximate Reasoning, 15(3):225-263, 1996.

  27. Finn V. Jensen. An Introduction to Bayesian Networks, UCL Press, 1996.

  28. Finn V. Jensen. Bayesian Networks and Decision Graphs, Springer, 2001.

  29. Michael I. Jordan. (ed.) Learning in Graphical Models, MIT Press, 1998.

  30. Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul. An Introduction to Variational Methods for Graphical Models, Machine Learning, 37(2):183-233, 1999.

  31. Daphne Koller and Brian Milch. Multi-Agent Influence Diagrams for Representing and Solving Games, Games and Economic Behavior, 45(1):181-221, 2003.

  32. Daphne Koller and Avi Pfeffer. Representations and Solutions for Game-Theoretic Problems, Artificial Intelligence, 94(1):167-215, 1997.

  33. Kevin B. Korb and Ann E. Nicholson. Bayesian Artificial Intelligence, CRC Press, 2004.

  34. S.L. Lauritzen, A.P. Dawid, B.N. Larsen, and H.G. Leimer. Independence Properties of Directed Markov Fields. Networks, 20:491-505, 1990.

  35. Michael Littman. Markov Games as a Framework for Multi-Agent Reinforcement Learning, Proceedings of the 11th International Conference on Machine Learning, 157-163, 1994.

  36. David J.C. MacKay. Introduction to Monte Carlo Methods, in Learning in Graphical Models, Michael I. Jordan, (ed.), 175-204, Kluwer, 1998.

  37. James E. Matheson. Using Influence Diagrams to Value Information and Control, in R.M. Oliver and J.Q. Smith, Influence Diagrams, Belief Nets and Decision Analysis, 25-63, Wiley, 1990.

  38. Richard E. Neapolitan. Probabilistic Reasoning in Expert Systems: Theory and Algorithms, Wiley, 1990.

  39. Richard E. Neapolitan. Learning Bayesian Networks, Prentice Hall, 2003.

  40. R. Oliver and Q. Smith. (eds.) Influence Diagrams, Belief Nets and Decision Analysis, Wiley, 1990.

  41. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan-Kaufmann, 1988.

  42. Judea Pearl. Reasoning with Cause and Effect, 16th International Joint Conference on Artificial Intelligence, 1437-1449, 1999.

  43. Judea Pearl. Reasoning with Cause and Effect, AI Magazine, 95-111, Spring 2002.

  44. Judea Pearl. Causality: Models, Reasoning, and Inference, Cambridge University Press, 2000.

  45. Runping Qi and David Poole. A New Method for Influence Diagram Evaluation Computational Intelligence, 11(1):498-528, 1995.

  46. Stuart Russell and Eric Wefald. Do the Right Thing: Studies in Limited Rationality, MIT Press, 1991.

  47. Stuart Russell. Rationality and intelligence, Artificial Intelligence, 94(1):57-77, 1997.

  48. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, Second Edition, Prentice Hall, 2003.

  49. Ross Shachter. Evaluating Influence Diagrams, Operations Research, 34:871-882, 1986.

  50. Ross D. Shachter. Bayes-Ball: The Rational Pastime, Fourteenth Conference on Uncertainty in Artificial Intelligence, 480-487, 1998.

  51. Glenn Shafer and Judea Pearl. (eds.) Readings in Uncertain Reasoning, Morgan Kaufmann, 1990.

  52. Glenn Shafer. A Mathematical Theory of Evidence, Princeton University Press, 1976.

  53. Herbert Simon. From Substantive to Procedural Rationality, In Herbert Simon, Models of Bounded Rationality, Volume 2, MIT Press, 1982.

  54. R. Smallwood and E. Sondik. The Optimal Control of Partially Observable Markov Processes over a Finite Horizon, Operations Research, 21:1071-1088, 1973.

  55. Richard Sutton and Andrew Barto. Reinforcement Learning, MIT Press, 1998.

  56. Sebastian Thrun. Probabilistic Algorithms in Robotics., AI Magazine, 21(4):93-109, 2000.

  57. Sebastian Thrun, Wolfram Burgard, and Dieter Fox. A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots, Machine Learning, 31(1-3):29-53, 1998.

  58. David S. Vaughan, Bruce M. Perrin, and Robert M. Yadrick. Comparing Expert Systems Built Using Different Uncertain Inference Systems, Fifth Conference on Uncertainty in Artificial Intelligence, 1989.

  59. T. Verma and J. Pearl. Causal Networks: Semantics and Expressiveness. In R.D. Shachter et al., editors, Uncertainty in Artificial Intelligence 469-76, 1990.

  60. J. von Neumann and O. Morgenstern. Theory of Games and Economic Behavior, Princeton University Press, 1944.

  61. Michael Wellman. Fundamental Concepts of Qualitative Probabilistic Networks, Artificial Intelligence, 44(3):257-303, 1990.

  62. Lotfi Zadeh. Fuzzy Sets as the Basis for a Theory of Possibility, Fuzzy Sets and Systems, 1:3-28, 1978.

  63. Shlomo Zilberstein. Using Anytime Algorithms in Intelligent Systems. AI Magazine, 17(3):73-83, 1996.

© 2004 Shlomo Zilberstein.