@Article{panait05a,
  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 =	 3,
  number =	 11,
  pages =	 {383--434},
  month =	 {November},
  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.},
  doi = 	 {10.1007/s10458-005-2631-2},
  issn = 	 {1573-7454},
  url = 	 {http://jmvidal.cse.sc.edu/library/panait05a.pdf}
}