Uncertainty in Artificial Intelligence
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Approximately Optimal Monitoring of Plan Preconditions
Craig Boutilier
Abstract:
Monitoring plan preconditions can allow for replanning when a precondition fails, generally far in advance of the point in the plan where the precondition is relevant. However, monitoring is generally costly, and some precondition failures have a very small impact on plan quality. We formulate a model for optimal precondition monitoring, using partially-observable Markov decisions processes, and describe methods for solving this model efficitively, though approximately. Specifically, we show that the single-precondition monitoring problem is generally tractable, and the multiple-precondition monitoring policies can be efficitively approximated using single-precondition soultions.
Keywords:
Pages: 54-62
PS Link:
PDF Link: /papers/00/p54-boutilier.pdf
BibTex:
@INPROCEEDINGS{Boutilier00,
AUTHOR = "Craig Boutilier ",
TITLE = "Approximately Optimal Monitoring of Plan Preconditions",
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 = "54--62"
}


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