Multi-strategy learning of search control for partial-order planning

摘要: 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 re nements. Speci cally, Scope learns domain-speci c control rules for a version of the UCPOP planning algorithm. The resulting system is shown to produce signi cant speedup in two di erent planning domains.