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• W2059289370 startingPage "387" @default.
• W2059289370 abstract "Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator—Instance-Based Crossover—that is able to use traces of the base planner as raw genetic material to be injected into the evolving population." @default.
• W2059289370 created "2016-06-24" @default.
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• W2059289370 creator A5047189142 @default.
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• W2059289370 date "2001-12-01" @default.
• W2059289370 modified "2023-09-23" @default.
• W2059289370 title "Learning to Solve Planning Problems Efficiently by Means of Genetic Programming" @default.
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• W2059289370 doi "https://doi.org/10.1162/10636560152642841" @default.
• W2059289370 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/11709102" @default.
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