article

Cooperative Multi-Agent Learning: The State of the Art

Authors:

Sean LukeAuthors Info & Claims

Autonomous Agents and Multi-Agent Systems, Volume 11, Issue 3

Pages 387 - 434

https://doi.org/10.1007/s10458-005-2631-2

Published: 01 November 2005 Publication History

Abstract

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems ( team learning ), or using multiple simultaneous learners, often one per agent ( concurrent learning ). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources.

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Index Terms

Cooperative Multi-Agent Learning: The State of the Art
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
  2. Machine learning

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cover image Autonomous Agents and Multi-Agent Systems

Autonomous Agents and Multi-Agent Systems Volume 11, Issue 3

November 2005

217 pages

ISSN:1387-2532

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Copyright © Copyright © 2005 Springer Science+Business Media, Inc.

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Published: 01 November 2005

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