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Machine Learning and Inductive Logic Programming for Multi-agent Systems

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Multi-Agent Systems and Applications (ACAI 2001)

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Abstract

Learning is a crucial ability of intelligent agents. Rather than presenting a complete literature review, we focus in this paper on important issues surrounding the application of machine learning (ML) techniques to agents and multi-agent systems (MAS). In this discussion we move from disembodied ML over single-agent learning to full multiagent learning. In the second part of the paper we focus on the application of Inductive Logic Programming, a knowledge-based ML technique, to MAS, and present an implemented framework in which multi-agent learning experiments can be carried out.

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Author information

Authors and Affiliations

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

    Dimitar Kazakov & Daniel Kudenko⋆

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  1. Dimitar Kazakov
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  2. Daniel Kudenko⋆
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Editor information

Editors and Affiliations

  1. Department of Electronics and Computer Science Highfield, University of Southampton, Southampton, SO17 1BJ, UK

    Michael Luck

  2. Facultiy of Electr. Engineering, Dept. of Cybernetics Technicka 2, Czech Technical University, 166 27, Prague 6, Czech Republic

    Vladimír Mařík  & Olga Štěpánková  & 

  3. Dept.of Med. Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6, 1010, Vienna, Austria

    Robert Trappl

  4. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, 1010, Vienna, Austria

    Robert Trappl

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© 2001 Springer-Verlag Berlin Heidelberg

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Kazakov, D., Kudenko⋆, D. (2001). Machine Learning and Inductive Logic Programming for Multi-agent Systems. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds) Multi-Agent Systems and Applications. ACAI 2001. Lecture Notes in Computer Science(), vol 2086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47745-4_11

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  • DOI: https://doi.org/10.1007/3-540-47745-4_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42312-6

  • Online ISBN: 978-3-540-47745-7

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