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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Citation:
In New Directions in AI Planning, Malik Ghallab and Alfredo Milani (Eds.), pp. 129-140, Amsterdam 1996. IOS Press.
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Tara Estlin Ph.D. Alumni Tara Estlin [at] jpl nasa gov
Raymond J. Mooney Faculty mooney [at] cs utexas edu