@article{stone00b,
  author =	 {Peter Stone and Manuela M. Veloso},
  title =	 {Multiagent Systems: A Survey from a Machine Learning
                  Perspective},
  googleid =	 {2JESs73TcuQJ:scholar.google.com/},
  journal =	 {Autonomous Robots},
  volume =	 8,
  number =	 3,
  pages =	 {345--383},
  year =	 2000,
  abstract =	 {Distributed Artificial Intelligence (DAI) has
                  existed as a subfield of AI for less than two
                  decades. DAI is concerned with systems that consist
                  of multiple independent entities that interact in a
                  domain. Traditionally, DAI has been divided into two
                  sub-disciplines: Distributed Problem Solving (DPS)
                  focuses on the information management aspects of
                  systems with several components working together
                  towards a common goal; Multiagent Systems (MAS)
                  deals with behavior management in collections of
                  several independent entities, or agents. This survey
                  of MAS is intended to serve as an introduction to
                  the field and as an organizational framework. A
                  series of general multiagent scenarios are
                  presented. For each scenario, the issues that arise
                  are described along with a sampling of the
                  techniques that exist to deal with them. The
                  presented techniques are not exhaustive, but they
                  highlight how multiagent systems can be and have
                  been used to build complex systems. When options
                  exist, the techniques presented are biased towards
                  machine learning approaches. Additional
                  opportunities for applying machine learning to MAS
                  are highlighted and robotic soccer is presented as
                  an appropriate test bed for MAS. This survey does
                  not focus exclusively on robotic systems. However,
                  we believe that much of the prior research in
                  non-robotic MAS is relevant to robotic MAS, and we
                  explicitly discuss several robotic MAS, including
                  all of those presented in this issue.},
  keywords =     {multiagent learning survey},
  url =		 {http://jmvidal.cse.sc.edu/library/stone00a.pdf},
  citeseer =	 {stone97multiagent.html},
  cluster = 	 {16461452399699202520}
}