Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis

TL;DR

This paper presents techniques for efficient decision-theoretic planning, focusing on the DRIPS system that uses abstraction and search control to improve planning performance, demonstrated through empirical evaluation on complex problems.

Contribution

It introduces methods for automatic search control generation and evaluates the DRIPS system's efficiency compared to other algorithms.

Findings

DRIPS with search control outperforms without in complex problems

Abstraction significantly improves planning efficiency

Empirical results show competitive performance against branch-and-bound algorithms

Abstract

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.