Agent community extraction for 2D-RoboSoccer

Abstract

Agents perform tasks to maximize their benefits. There are several instances where the agent can not perform a task individually. In these situations, agents need to cooperate and coordinate with other agents effectively and efficiently to maximize their benefits in a limited time. In several domains, we can analyze the behavior of successful agents and the way they interact with other agents forming strong communities or coalitions. This knowledge can be used by a new or unsuccessful agent to collaborate with other agents that gives maximum benefit under strict time constraints. This paper proposes a generic procedure for extracting these hidden communities that can be used by the agents in a productive manner. We tested the framework on robosoccer simulation environment and our experiments indeed show drastic increase in both agent and team performance. © Springer-Verlag Berlin Heidelberg 2006.