New Directions in AI Planning, M. Ghallab and A. Milani (Eds.), IOS Press, 1996, pp. 129-140.
Paper ID: 52
Category: Learning for Planning and Problem Solving
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.
