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Evolving heuristics for planning

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Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

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Abstract

In this paper we describe EvoCK, 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 efficiency. We also analyze two ways to introduce domain knowledge acquired by another method (Hamlet) into EvoCK: 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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Author information

Authors and Affiliations

  1. Universidad Carlos III de Madrid, Butarque 15, 28911, Leganés (Madrid), Espa¯na

    Ricardo Aler, Daniel Borrajo & Pedro Isasi

Authors
  1. Ricardo Aler
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  2. Daniel Borrajo
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  3. Pedro Isasi
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Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Aler, R., Borrajo, D., Isasi, P. (1998). Evolving heuristics for planning. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040825

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  • DOI: https://doi.org/10.1007/BFb0040825

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

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