Uncertainty in Artificial Intelligence
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Suboptimality Bounds for Stochastic Shortest Path Problems
Eric Hansen
Abstract:
We consider how to use the Bellman residual of the dynamic programming operator to compute suboptimality bounds for solutions to stochastic shortest path problems. Such bounds have been previously established only in the special case that "all policies are proper," in which case the dynamic programming operator is known to be a contraction, and have been shown to be easily computable only in the more limited special case of discounting. Under the condition that transition costs are positive, we show that suboptimality bounds can be easily computed even when not all policies are proper. In the general case when there are no restrictions on transition costs, the analysis is more complex. But we present preliminary results that show such bounds are possible.
Keywords:
Pages: 301-310
PS Link:
PDF Link: /papers/11/p301-hansen.pdf
BibTex:
@INPROCEEDINGS{Hansen11,
AUTHOR = "Eric Hansen ",
TITLE = "Suboptimality Bounds for Stochastic Shortest Path Problems",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "301--310"
}


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