Learning strategy knowledge incrementally

Abstract

Modern industrial processes require advanced computer tools that should adapt to the user requirements and to the tasks being solved. Strategy learning consists of automating the acquisition of patterns of actions used while solving particular tasks. Current intelligent strategy learning systems acquire operational knowledge to improve the efficiency of a particular problem solver. However, these strategy learning tools should also provide a way of achieving low-cost solutions according to user-specific criteria. In this paper, we present a learning system, HAMLET, which is integrated in a planning architecture, PRODIGY, and acquires control knowledge to guide PRODIGY to efficiently produce cost-effective plans. HAMLET learns from planning episodes, by explaining why the correct decisions were made, and later refines the learned strategy knowledge to make it incrementally correct with experience.