Learning search control knowledge to improve plan quality
M. Alicia Pérez · 1996
Generating good, production-qualityplans is an essential element in transforming planners from research tools into real-world applications, but one that has been frequently overlooked in research on machine learning for planning systems. Most work has been aimed at improving the efficiency of planning ("speed-up learning") or at acquiring or refining domain knowledge. This thesis focuses on learning search-control knowledge to improve the quality of the plans produced by the planner. Knowledge about plan quality in a domain comes in two forms: (a) a post-facto quality metric that computes the quality (e.g. the execution cost) of a plan, and (b) planning-time decision-control knowledge used to guide the planner towards producing higher-quality plans. The first kind of knowledge is not operational until after a plan is produced, but is exactly the kind typically available, in contrast to the far more complex operational decision-time knowledge. Learning operational quality control knowl...