Uncertainty in Artificial Intelligence
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Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis
Peter Haddawy, AnHai Doan, Richard Goodwin
This paper discusses techniques for performing efficient decision-theoretic planning. We give an overview of the DRIPS decision-theoretic refinement planning system, which uses abstraction to efficiently identify optimal plans. We present techniques for automatically generating search control information, which can significantly improve the planner's performance. We evaluate the efficiency of DRIPS both with and without the search control rules on a complex medical planning problem and compare its performance to that of a branch-and-bound decision tree algorithm.
Keywords: Decision-theoretic planning, abstraction, stochastic dominance.
Pages: 229-236
PS Link: http://www.cs.uwm.edu/faculty/haddawy/papers.html
PDF Link: /papers/95/p229-haddawy.pdf
AUTHOR = "Peter Haddawy and AnHai Doan and Richard Goodwin",
TITLE = "Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "229--236"

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