Integrating EBL and ILP to Acquire Control Rules for Planning*

Tara A. Estlin and Raymond J. Mooney

Most approaches to learning control information in pla~nlng systems use explanation-based learning to generate control rules. Unfortunately, EBL alone often produces overly complex rules that actually decrease planning efficiency. This paper presents a novel learning approach for control knowledge acquisition that integrates explanation-based learning with techniques from inductive logic programming. EBL is used to constrain an inductive search for selection heuristics that help a planner choose between competing plan refinements. ScoPE is one of the few systems to address learning control information in the newer partial-order planning. 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 plannlng domains.

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