Most approaches to learning control information in planning 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 planners. 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 planning domains.

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Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96) (1996), pp. 271--279.

@article{estlin:msl96, title={Integrating EBL and ILP to Acquire Control Rules for Planning}, author={Tara A. Estlin and Raymond J. Mooney}, booktitle={Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96)}, month={May}, address={Harpers Ferry, WV}, pages={271--279}, url="http://www.cs.utexas.edu/users/ai-lab?estlin:msl96", year={1996} }