Partially Observable Markov Decision Processes

Tony's POMDP Research Papers

Refereed papers

Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling, and Anthony R. Cassandra. "Solving POMDPs by Searching the Space of Finite Policies." Proceedings of the Fifteenth International Conference on Uncertainty in Artificial Intelligence. 1999. (postscript, 10 pages, 154K bytes)

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), (Brown Technical Report)

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.) ( Brown Technical Report)

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) ( Brown Technical Report )

Technical Reports

Anthony Cassandra. A Survey of POMDP Applications. Presented at the AAAI Fall Symposium, 1998. (PDF, 9 pages, 140K bytes)

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) ( Brown Technical Report)

Michael Littman, Anthony Cassandra, and Leslie Kaelbling. Learning policies for partially observable environments: Scaling up. (Expanded form of 1995 Machine Learning paper.) (compressed postscript, 59 pages, 266K bytes)

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) (Brown Technical Report )

Drafts and Notes

Nevin L. Zhang, Michael L. Littman, Anthony R. Cassandra. Incremental Pruning: Solving MDPs with Incomplete State Information. Rough draft of a paper intended for submission to an Operations Research Journal. Never completed, circa 1997. (postscript, 22 pages, 545K bytes)

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

Last modified: Sun Jul 26 14:25:16 MDT 2009