The Knowledge Engineering Review

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Learning in multi-agent systems


EDUARDO  ALONSO a1, MARK  D'INVERNO a2, DANIEL  KUDENKO a3, MICHAEL  LUCK a4 and JASON  NOBLE a5
a1 Department of Computing, City University, UK
a2 Cavendish School of Computer Science, University of Westminster, UK
a3 Department of Computer Science, University of York, UK
a4 Department of Electronics and Computer Science, University of Southampton, UK
a5 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.