Uncertainty in Artificial Intelligence
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A Probabilistic Model of Action for Least-Commitment Planning with Information Gather
Denise Draper, Steve Hanks, Daniel Weld
Abstract:
AI planning algorithms have addressed the problem of generating sequences of operators that achieve some input goal, usually assuming that the planning agent has perfect control over and information about the world. Relaxing these assumptions requires an extension to the action representation that allows reasoning both about the changes an action makes and the information it provides. This paper presents an action representation that extends the deterministic STRIPS model, allowing actions to have both causal and informational effects, both of which can be context dependent and noisy. We also demonstrate how a standard least-commitment planning algorithm can be extended to include informational actions and contingent execution.
Keywords:
Pages: 178-186
PS Link:
PDF Link: /papers/94/p178-draper.pdf
BibTex:
@INPROCEEDINGS{Draper94,
AUTHOR = "Denise Draper and Steve Hanks and Daniel Weld",
TITLE = "A Probabilistic Model of Action for Least-Commitment Planning with Information Gather",
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 = "178--186"
}


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