Mean-based Heuristic Search for Real-Time Planning

TL;DR

This paper presents MHSP, a novel mean-based heuristic search algorithm for real-time planning that combines bandit algorithms with heuristic search, demonstrating faster and higher-quality plans than existing methods.

Contribution

Introduction of MHSP, a new real-time planning algorithm combining UCT principles with heuristic search, showing improved speed and plan quality.

Findings

MHSP produces plans faster than existing algorithms.

Plans generated by MHSP tend to be closer to optimal over time.

MHSP outperforms in various planning problem evaluations.

Abstract

In this paper, we introduce a new heuristic search algorithm based on mean values for real-time planning, called MHSP. It consists in associating the principles of UCT, a bandit-based algorithm which gave very good results in computer games, and especially in Computer Go, with heuristic search in order to obtain a real-time planner in the context of classical planning. MHSP is evaluated on different planning problems and compared to existing algorithms performing on-line search and learning. Besides, our results highlight the capacity of MHSP to return plans in a real-time manner which tend to an optimal plan over the time which is faster and of better quality compared to existing algorithms in the literature.