Hybrid learning of search control for partial-order planning
Tara A. Estlin, Raymond J. Mooney · 1996
. This paper presents results on applying a version of the Dolphin search-control learning system to speed up a partial-order planner. Dolphin integrates explanation-based and inductive learning techniques to acquire effective clause-selection rules for Prolog programs. A version of the UCPOP partial-order planning algorithm has been implemented as a Prolog program and Dolphin used to automatically learn domain-specific search control rules that help eliminate backtracking. The resulting system is shown to produce significant speedup in several planning domains. 1 Introduction Efficient planning often requires domain-specific search heuristics; however, constructing appropriate heuristics for a new domain is a difficult, laborious task. Research in learning and planning attempts to address this important problem by developing methods that automatically acquire search-control knowledge from experience. However, most work in learning and planning has been in the context of linear, state...