@Article{bulka07a,
  author =	 {Blazej Bulka and Matthew Gaston and Marie des
                  Jardins},
  title =	 {Local strategy learning in networked multi-agent
                  team formation},
  journal =	 {Autonomous Agents and Multi-Agent Systems},
  year =	 2007,
  volume =	 15,
  number =	 1,
  pages =	 {29--45},
  issn =	 {1387-2532},
  doi =		 {10.1007/s10458-006-0007-x},
  publisher =	 {Kluwer Academic Publishers},
  address =	 {Hingham, MA, USA},
  abstract =	 {Networked multi-agent systems are comprised of many
                  autonomous yet interdependent agents situated in a
                  virtual social network. Two examples of such systems
                  are supply chain networks and sensor networks. A
                  common challenge in many networked multi-agent
                  systems is decentralized team formation among the
                  spatially and logically extended agents. Even in
                  cooperative multi-agent systems, efficient team
                  formation is made difficult by the limited local
                  information available to the individual agents. We
                  present a model of distributed multi-agent team
                  formation in networked multi-agent systems, describe
                  a policy learning framework for joining teams based
                  on local information, and give empirical results on
                  improving team formation performance. In particular,
                  we show that local policy learning from limited
                  information leads to a significant increase in
                  organizational team formation performance compared
                  to a random policy.},
  url = 	 {http://jmvidal.cse.sc.edu/library/bulka07a.pdf},
  keywords = 	 {multiagent learning},
  cluster = 	 {15008312953892129231}
}