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Learning in multi-agent systems

Alonso, E, D'Inverno, M, Kudenko, D, Luck, M and Noble, J (2001) Learning in multi-agent systems. The Knowledge Engineering Review. pp. 277-284. ISSN 1469-8005

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Abstract

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

Item Type: Article
Copyright, Publisher and Additional Information: © 2001 Cambridge University Press
Keywords: BEHAVIOR
Institution: The University of York
Academic Units: The University of York > Computer Science (York)
Depositing User: Repository Officer
Date Deposited: 05 Jun 2006
Last Modified: 15 Oct 2014 10:46
Published Version: http://dx.doi.org/10.1017/S0269888901000170
Status: Published
Refereed: Yes
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/1227

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