Skip to main content
SpringerLink
Log in

Learning Action Strategies for Planning Domains Using Genetic Programming

  • Conference paper
  • First Online:
Applications of Evolutionary Computing (EvoWorkshops 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2611))

Included in the following conference series:

Abstract

There are many different approaches to solving planning problems, one of which is the use of domain specific control knowledge to help guide a domain independent search algorithm. This paper presents L2Plan which represents this control knowledge as an ordered set of control rules, called a policy, and learns using genetic programming. The genetic program’s crossover and mutation operators are augmented by a simple local search. L2Plan was tested on both the blocks world and briefcase domains. In both domains, L2Plan was able to produce policies that solved all the test problems and which outperformed the hand-coded policies written by the authors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aler, R., Borrajo, D. and Isasi, P. (1998). Genetic programming of control knowledge for planning. In Proceedings of AIPS-98, Pittsburgh, PA.

    Google Scholar 

  2. Aler, R., Borrajo, D. and Isasi, P. (2001). Learning to Solve Problems Efficiently by Means of Genetic Programming. Evolutionary Computation 9(4), 387–420.

    Article  Google Scholar 

  3. Borrajo, D., and Veloso, M. (1997) Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review 11(1–5), 371–405.

    Google Scholar 

  4. Katukam, S. and Kambhampati, S. (1994). Learning explanation-based search control rules for partial order planning. In Proceedings of the Twelfth National Conference on Artificial Intelligence, 582–587, Seattle,WA. AAAI Press.

    Google Scholar 

  5. Khardon, R. (1996). Learning to take actions. In Proc. National Conference on Artificial Intelligence (AAAI-96), 787–792. AAAI Press.

    Google Scholar 

  6. Khardon, R. (1999). Learning action strategies for planning domains. Artificial Intelligence 113(1–2), 125–148.

    Article  MATH  Google Scholar 

  7. Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press.

    Google Scholar 

  8. Leckie, C. and Zukerman, I. (1991). Learning search control rules for planning: An inductive approach. In Proceedings of Machine LearningWorkshop, 422–426.

    Google Scholar 

  9. Rivest, R. L. (1987). Learning decision lists. Machine Learning 2(3), 229–246.

    Google Scholar 

  10. Spector, L. (1994). Genetic programming andAI planning systems. In Proceedings of Twelfth National Conference on Artificial Intelligence, Seattle, Washington, USA, 1994. AAAI Press/MIT Press.

    Google Scholar 

  11. Veloso, M., Carbonell, J., Perez, M., Borrajo, D., Fink, E., and Blythe, J. (1995). Integrating planning and learning: The PRODIGY architecture. Journal of Experimental and Theoretical Artificial Intelligence 7, 81–120.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Intelligent Systems and their Applications, School of Informatics, University of Edinburgh, 80 South Bridge, EH1 1HN, Edinburgh

    John Levine & David Humphreys

Authors
  1. John Levine
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Humphreys
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Engineering, University of Parma, Parco Area delle Scienze 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  2. Computing Laboratory, University of Kent, Canterbury, Kent, CT2 7NF, UK

    Colin G. Johnson

  3. Dept. of Information and Communications Technologies Faculty of Computer Science, University of A Coruña, A Coruña, CP 15071, Spain

    Juan J. Romero Cardalda

  4. Dept. of Mathematics and Computer Science, Free University of Amsterdam, de Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands

    Elena Marchiori

  5. Schol of Systems Engineering, University of Reading, PO Box 225, Whiteknights, Reading, RG6 6AY, UK

    David W. Corne

  6. AnimatLab, 8 rue du capitaine Scott, 75015, Paris, France

    Jean-Arcady Meyer  & Agnès Guillot  & 

  7. SAP AG, Neurottstrasse 16, 69190, Walldorf, Germany

    Jens Gottlieb

  8. Parallel Computing and Complex Systems Group, University of Leipzig, Augustusplatz 10/11, 04109, Leipzig, Germany

    Martin Middendorf

  9. Algorithms and Data Structures Group Institute of Computer Graphics, Vienna University of Technology, Favoritenstrasse 9-11/186, 1040, Vienna, Austria

    Günther R. Raidl

  10. School of Computing, Napier University, 219 Colinton Road, Edinburgh, EH14 1DJ, UK

    Emma Hart

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Levine, J., Humphreys, D. (2003). Learning Action Strategies for Planning Domains Using Genetic Programming. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_62

Download citation

  • DOI: https://doi.org/10.1007/3-540-36605-9_62

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00976-4

  • Online ISBN: 978-3-540-36605-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation