Multi-Strategy Learning of Search Control for Partial-Order Planning

Abstract

Most research in planning and learning has involved linear, state-based planners. This paper presents Scope, a system for learning search-control rules that improve the performance of a partial-order planner. Scope integrates explanation-based and inductive learning techniques to acquire control rules for a partial-order planner. Learned rules are in the form of selection heuristics that help the planner choose between competing plan refinements. Specifically, Scope learns domain-specific control rules for a version of the UCPOP planning algorithm. The resulting system is shown to produce significant speedup in two different planning domains. Introduction Efficient planning often requires domain-specific search heuristics; however, constructing appropriate heuristics for a new domain is a difficult task. Research in learning and planning attempts to address this problem by developing methods that automatically acquire search-control knowledge from experience. Most work has been in ...