2013/02/20 by Peter Haddawy, Haddawy, Peter, AnHai Doan +3
Computer Science · Decision Sciences · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Complex Systems and Decision Making #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.4952
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
arxiv created 2013/02/20 · openalex publication_date 2013/02/20 · arxiv updated 2013/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.