Uncertainty in Artificial Intelligence
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Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method
Nevin Zhang, Stephen Lee
Abstract:
There is much interest in using partially observable Markov decision processes (POMDPs) as a formal model for planning in stochastic domains. This paper is concerned with finding optimal policies for POMDPs. We propose several improvements to incremental pruning, presently the most efficient exact algorithm for solving POMDPs.
Keywords: Partially observable Markov decision processes, exact algorithms, incremental pruning
Pages: 523-530
PS Link: http://www.cs.ust.hk/~lzhang/paper/uai98stephen.html
PDF Link: /papers/98/p523-zhang.pdf
BibTex:
@INPROCEEDINGS{Zhang98,
AUTHOR = "Nevin Zhang and Stephen Lee",
TITLE = "Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "523--530"
}


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