Uncertainty in Artificial Intelligence
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Value-Directed Sampling Methods for POMDPs
Pascal Poupart, Luis Ortiz, Craig Boutilier
Abstract:
We consider the problem of approximate belief-state monitoring using particle filtering for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP). While particle filtering has become a widely-used tool in AI for monitoring dynamical systems, rather scant attention has been paid to their use in the context of decision making. Assuming the existence of a value function, we derive error bounds on decision quality associated with filtering using importance sampling. We also describe an adaptive procedure that can be used to dynamically determine the number of samples required to meet specific error bounds. Empirical evidence is offered supporting this technique as a profitable means of directing sampling effort where it is needed to distinguish policies.
Keywords:
Pages: 453-461
PS Link:
PDF Link: /papers/01/p453-poupart.pdf
BibTex:
@INPROCEEDINGS{Poupart01,
AUTHOR = "Pascal Poupart and Luis Ortiz and Craig Boutilier",
TITLE = "Value-Directed Sampling Methods for POMDPs",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "453--461"
}


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