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Machine Learning for Logic-Based Multi-agent Systems

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Formal Approaches to Agent-Based Systems (FAABS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1871))

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Abstract

When developing a Multi-Agent System (MAS), it is very difficult and sometimes even impossible to foresee all potential situations the agents could encounter and specify their behaviour in advance. Therefore it is widely recognised that one of the more important features of high level agents is their capability to adapt and learn.

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References

  1. T.M. Mitchell, R. Keller, and S. Kedar-Cabelli. Explanation-based generalization: A unifying view. Machine Learning, 1:4–80, 1986.

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  2. S. Muggleton and L. de Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19:629–679, 1994.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, York, YO10 5DD, UK

    Eduardo Alonso & Daniel Kudenko

Authors
  1. Eduardo Alonso
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  2. Daniel Kudenko
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Editor information

Editors and Affiliations

  1. NASA Goddard Space Flight Center, Greenbelt, Maryland, 20771, USA

    James L. Rash , Walt Truszkowski  & Michael G. Hinchey ,  & 

  2. Science Applications International Corporation, 4001 Fairfax Drive, Suite 300, Airlington, VA, 22203, USA

    Christopher A. Rouff

  3. Naval Research Laboratory, 4555 Overlook Avenue, S.W., Washington, D.C., USA

    Diana Gordon

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© 2001 Springer-Verlag Berlin Heidelberg

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Alonso, E., Kudenko, D. (2001). Machine Learning for Logic-Based Multi-agent Systems. In: Rash, J.L., Truszkowski, W., Hinchey, M.G., Rouff, C.A., Gordon, D. (eds) Formal Approaches to Agent-Based Systems. FAABS 2000. Lecture Notes in Computer Science(), vol 1871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45484-5_28

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  • DOI: https://doi.org/10.1007/3-540-45484-5_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42716-2

  • Online ISBN: 978-3-540-45484-7

  • eBook Packages: Springer Book Archive

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