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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