An Analysis of Learning to Plan as a Search Problem

COMPOSER is one of a growing number of techniques for learning to plan. Like other approaches, it embodies a number of simplifications to overcome the complexities of learning. These simplifications introduce tradeoffs between learning efficiency and effectiveness. In this paper we relate COMPOSER to our general framework of simplifications for learning to plan [Gratch92a]. This discussion illustrates how such a framework may be used to analyzea particular approach, highlighting the learning system's strengths and weaknesses. 1 INTRODUCTION In machine learning there is considerable interest in techniqueswhichimproveplanning ability. Investigation in this area has identified a wide array of techniques including macro --operators [DeJong86, Fikes72, Mitchell86, Segre88], chunks [Laird86], and control rules [Minton88, Mitchell83 ]. With these techniques comes a growing battery of successful demonstrations in domains ranging from 8--puzzle to space shuttle payload processing. Unfortunatel..