Uncertainty in Artificial Intelligence
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Value-Directed Belief State Approximation for POMDPs
Pascal Poupart, Craig Boutilier
Abstract:
We consider the problem belief-state monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief state. Other schemes for belief-state approximation (e.g., based on minimixing a measures such as KL-diveregence between the true and estimated state) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed approximation schemes, where approximation quality is determined by the expected error in utility rather than by the error in the belief state itself. We propose heuristic methods for finding good projection schemes for belief state estimation - exhibiting anytime characteristics - given a POMDP value fucntion. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief state approximation.
Keywords:
Pages: 497-506
PS Link:
PDF Link: /papers/00/p497-poupart.pdf
BibTex:
@INPROCEEDINGS{Poupart00,
AUTHOR = "Pascal Poupart and Craig Boutilier",
TITLE = "Value-Directed Belief State Approximation for POMDPs",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "497--506"
}


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