Cooperative Multi-Agent Learning: The State of the Art

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Cooperative Multi-Agent Learning: The State of the Art (2005)

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by Liviu Panait , Sean Luke

Venue:	Autonomous Agents and Multi-Agent Systems
Citations:	180 - 8 self

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@ARTICLE{Panait05cooperativemulti-agent,
    author = {Liviu Panait and Sean Luke},
    title = {Cooperative Multi-Agent Learning: The State of the Art},
    journal = {Autonomous Agents and Multi-Agent Systems},
    year = {2005},
    volume = {11},
    pages = {2005}
}

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Abstract

Cooperative multi-agent systems 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 multi-agent systems 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 or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including reinforcement learning, 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. 1

Keyphrases

cooperative multi-agent learning reinforcement learning behavioral sophistication optimization process previous survey concurrent learning multiple simultaneous learner indirect communication multi-agent problem complexity complex system evolutionary computation agent modeling multi-agent learning resource cooperative multi-agent learning literature several agent attempt adaptive dynamic multi-agent learning problem domain broad view single learner multi-agent system problem special issue broad survey open issue specific subareas joint solution task decomposition multi-agent problem game theory cooperative multi-agent system

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