Learning Plan Rewriting Rules
نویسندگان
چکیده
Planning by Rewriting (PbR) is a new paradigm for efcient high-quality planning that exploits plan rewriting rules and e cient 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 de ne a set of domain-speci c plan rewriting rules which can be di cult 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-speci ed ones, and in several cases the learning algorithm discovered novel rewriting rules.
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