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Tony's POMDP Research Papers

Refereed papers

Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra. Planning and acting in partially observable stochastic domains. Artificial Intelligence, Volume 101, pp. 99-134, 1998. (compressed postscript, 45 pages, 362K bytes), (TR version )

Anthony R. Cassandra. Exact and Approximate Algorithms for Partially Observable Markov Decision Processes. Ph.D. Thesis. Brown University, Department of Computer Science, Providence, RI, 1998. ( compressed postscript, 474 pages, 703K bytes.)

Anthony R. Cassandra, Michael L. Littman and Nevin L. Zhang. Incremental pruning: A simple, fast, exact method for partially observable Markov decision processes. Uncertainty in Artificial Intelligence (UAI), 1997. ( compressed postscript, 8 pages, 75K bytes.)

Anthony R. Cassandra, Leslie Pack Kaelbling and James A. Kurien. Acting under uncertainty: Discrete Bayesian models for mobile robot navigation. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1996. ( compressed postscript, 9 pages, 124K bytes.) TR version

Michael Littman, Anthony Cassandra, and Leslie Kaelbling. Learning policies for partially observable environments: Scaling up. In Armand Prieditis and Stuart Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 362--370, San Francisco, CA, 1995. Morgan Kaufmann. (compressed postscript, 9 pages, 93K bytes)

Anthony R. Cassandra, Leslie Pack Kaelbling, and Michael L. Littman. Acting optimally in partially observable stochastic domains. In Proceedings of the Twelfth National Conference on Artificial Intelligence, (AAAI) Seattle, WA, 1994. (compressed postscript, 6 pages, 104K bytes) ( TR version )

Technical Reports

Michael L. Littman, Anthony R. Cassandra, and Leslie Pack Kaelbling. Efficient dynamic-programming updates in partially observable Markov decision processes. Submitted to Operations Research and rejected. (compressed postscript, 31 pages, 125K bytes) ( TR version)

Michael Littman, Anthony Cassandra, and Leslie Kaelbling. Learning policies for partially observable environments: Scaling up. Brown University, Department of Computer Science Technical Report CS-95-11 (Expanded form of 1995 Machine Learning paper.) (compressed postscript, 59 pages, 266K bytes) (TR version )

Anthony Cassandra. Optimal Policies for Partially Observable Markov Decision Processes. Technical Report CS-94-14, Brown University, Department of Computer Science, Providence RI, 1994. (compressed postscript, 100 pages, 650K bytes) (TR version )

Drafts and Notes

Anthony Cassandra. Incremental Pruning Technical Notes (unfinished). (compressed postscript, 28 pages, 82K bytes)

Last updated: 01/31/99