Uncertainty in Artificial Intelligence
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Operator Selection While Planning Under Uncertainty
Todd Mansell, Grahame Smith
Abstract:
This paper describes the best first search strategy used by U-Plan (Mansell 1993a), a planning system that constructs quantitatively ranked plans given an incomplete description of an uncertain environment. U-Plan uses uncertain and incomplete evidence de scribing the environment, characterizes it using a Dempster-Shafer interval, and generates a set of possible world states. Plan construction takes place in an abstraction hierarchy where strategic decisions are made before tactical decisions. Search through this abstraction hierarchy is guided by a quantitative measure (expected fulfillment) based on decision theory. The search strategy is best first with the provision to update expected fulfillment and review previous decisions in the light of planning developments. U-Plan generates multiple plans for multiple possible worlds, and attempts to use existing plans for new world situations. A super-plan is then constructed, based on merging the set of plans and appropriately timed knowledge acquisition operators, which are used to decide between plan alternatives during plan execution.
Keywords:
Pages: 423-431
PS Link:
PDF Link: /papers/94/p423-mansell.pdf
BibTex:
@INPROCEEDINGS{Mansell94,
AUTHOR = "Todd Mansell and Grahame Smith",
TITLE = "Operator Selection While Planning Under Uncertainty",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "423--431"
}


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