José Ambite, Craig A. Knoblock, and Steven Minton
Planning by Rewriting (PbR) is a new paradigm for efficient high-quality planning that exploits plan rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. Despite the advantages of PbR in terms of scalability, plan quality, and anytime behavior, PbR requires the user to define a set of domain-specific plan rewriting rules which can be difficult and time-consuming. This paper presents an approach to automatically learning the plan rewriting rules based on comparing initial and optimal plans. We report results for several planning domains showing that the learned rules are competitive with manually-specified ones, and in several cases the learning algorithm discovered novel rewriting rules.