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.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.