Learning from a Domain Expert to Generate Good Plans

This paper describes quality, a domainindependent architecture that learns operational quality-improving search-control knowledge given a domain theory, a domain-specific metric of plan quality, and problems which provide planning experience. quality can (optionally) interact with a human expert in the planning application domain who suggests improvements to the plans at the operator (plan step) level. The framework includes two distinct domain-independent learning mechanisms which differ in the language used to represent the learned knowledge, namely control rules and control knowledge trees, and in the kinds of quality metrics for which they are best suited. quality is fully implemented on top of the prodigy4.0 nonlinear planner and its empirical evaluation has shown that the learned knowledge is able to substantially improve plan quality. 1 Introduction In spite of advances in knowledge acquisition techniques and tools, acquiring knowledge, and in particular acquiring control knowl..