Evolving Heuristics for Planning


In this paper we describe GPlan, a new approach to the application of Genetic Programming (GP) to planning. This approach starts with a traditional AI planner (prodigy) and uses GP to acquire control rules to improve its eciency. We also analyze two ways to introduce domain knowledge acquired by another method (hamlet) into GPlan: seeding the initial population and using a new operator (knowledge-based Crossover). This operator combines genetic material from both an evolving population and a non-evolving population containing background knowledge. We tested these ideas in the blocksworld domain and obtained excellent results

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