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.

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In New Directions in AI Planning, Malik Ghallab and Alfredo Milani (Eds.), pp. 129-140, Amsterdam 1996. IOS Press.

@InCollection{estlin:bkchapter96, title={Hybrid Learning of Search Control for Partial-Order Planning}, author={Tara A. Estlin and Raymond J. Mooney}, booktitle={New Directions in AI Planning}, editor={Malik Ghallab and Alfredo Milani}, address={Amsterdam}, publisher={IOS Press}, pages={129-140}, url="http://www.cs.utexas.edu/users/ai-lab?estlin:bkchapter96", year={1996} }