Integrating ebl and ilp to acquire control rules for planning

摘要: 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 p] annFxs. Specifically, SCOPE learns domain-specific control rules for a version of the UCPOP p) anning algorithm. The resulting system is shown to produce significant speedup in two different p~ annlng domalnA.