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Learning in multi-agent systems

Published online by Cambridge University Press:  15 February 2002

EDUARDO ALONSO
Affiliation:
Department of Computing, City University, UK
MARK D'INVERNO
Affiliation:
Cavendish School of Computer Science, University of Westminster, UK
DANIEL KUDENKO
Affiliation:
Department of Computer Science, University of York, UK
MICHAEL LUCK
Affiliation:
Department of Electronics and Computer Science, University of Southampton, UK
JASON NOBLE
Affiliation:
School of Computing, University of Leeds, UK

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.

Type
Research Article
Copyright
© 2001 Cambridge University Press

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