Multi-Strategy Learning of Search Control for Partial-Order Planning (1996)
Most research in planning and learning has involved linear, state-based planners. This paper presents SCOPE, a system for learning search-control rules that improve the performance of a partial-order planner. SCOPE integrates explanation-based and inductive learning techniques to acquire control rules for a partial-order planner. Learned rules are in the form of selection heuristics that help the planner choose between competing plan refinements. 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.
In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 843-848, Portland, OR, August 1996.

Tara Estlin Ph.D. Alumni Tara Estlin [at] jpl nasa gov
Raymond J. Mooney Faculty mooney [at] cs utexas edu