An introduction to congregating in multi-agent systems

We present congregating both as a metaphor for describing and modeling multi-agent systems (MAS) and as a means for reducing coordination costs. We show how congregations can be used to explain and predict the behavior of self-interested agents that are searching for other agents to interact with. This framework is integrated with VidaI and Durfee's CLRI framework (1998) for evaluating learning within MAS. We provide experimental and analytical results which describe how the difficulty of the congregating problem increases exponentially with the number of agents, and present a solution to this in the form of labelers, which are agents that assign a description to a congregation, thereby reducing agents' search problem.