@Article{brooks02a,
  author =	 {Christopher H. Brooks and Edmund H. Durfee},
  title =	 {Congregation Formation in Multiagent Systems},
  googleid =	 {0EkTWmdxIK4J:scholar.google.com/},
  journal =	 {Journal of Autonomous Agents and Multi-agent
                  Systems},
  year =	 2003,
  volume =	 {7},
  number =	 {1--2},
  pages =	 {145--170},
  abstract =	 {We present congregating both as a metaphor for
                  describing and modeling multiagent systems (MAS) and
                  as a means for reducing coordination costs in
                  large-scale MAS. When agents must search for other
                  agents to interact with, congregations provide a way
                  for agents to bias this search towards groups of
                  agents that have tended to produce successful
                  interactions in the past. This causes each agent's
                  search problem to scale with the size of a
                  congregation rather than the size of the population
                  as a whole. In this paper, we present a formal model
                  of a congregation and then apply Vidal and Durfee's
                  CLRI framework [24] to the congregating problem. We
                  apply congregating to the affinity group domain, and
                  show that if agents are unable to describe
                  congregations to each other, the problem of forming
                  optimal congregations grows exponentially with the
                  number of agents. The introduction of labelers
                  provides a means of coordinating agent decisions,
                  thereby reducing the problem's complexity. We then
                  show how a structured label space can be exploited
                  to simplify the labeler's decision problem and make
                  the congregating problem linear in the number of
                  labels. We then present experimental evidence
                  demonstrating how congregating can be used to reduce
                  agents' search costs, thereby allowing the system to
                  scale up. We conclude with a comparison to other
                  methods for coordinating multiagent behavior,
                  particularly teams and coalitions.},
  keywords =     {multiagent learning},
  url =		 {http://jmvidal.cse.sc.edu/library/brooks02a.ps},
  cluster = 	 {12547153250560985552}
}