Formulation of tradeoffs in planning under uncertainty
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UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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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.