Learning Hierarchically Clustered Shared Plan Abstractions as Problem Solving Knowledge with High Utility for Planning

Complex problem solving can be substantially improved by the reuse of experience from previously solved problems. This requires that case libraries of successful problem solutions are transformed into problem solving knowledge with high utility, i.e. knowledge which causes high savings in search time, high application probability and low matching costs. Planning can be improved by explanation-based learning (EBL) of abstract plans from detailed, successfully solved planning problems. Abstract plans, expressed in well-established terms of the domain, serve as useful problem decompositions which can drastically reduce the planning complexity. Abstractions which are valid for a class of planning cases rather than for a single case, ensure a successful application in a larger spectrum of new situations. The hierarchical organization of the learned shared abstractions causes low matching costs. The presented S-PABS procedure is a combination of EBL and incremental conceptual clustering approaches to automatically construct a hierarchy of shared abstract plans by analyzing concrete planning cases.