Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis
This work addresses planning efficiency for AI systems, particularly in complex domains like medical planning, but appears incremental as it builds on existing decision-theoretic methods.
The paper tackles efficient decision-theoretic planning by presenting the DRIPS system, which uses abstraction and automatically generated search control rules to improve performance, and evaluates it on a medical planning problem, showing significant improvements compared to a branch-and-bound algorithm.
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.