Extracting reputation in multi agent systems by means of social network topology
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Behavior Classification with Self-Organizing Maps
RoboCup 2000: Robot Soccer World Cup IV
A Cybernetic Approach to the Modeling of Agent Communities
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
A method for decentralized clustering in large multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
On Safe Kernel Stable Coalition Forming among Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
A kernel-oriented model for coalition-formation in general environments: implementation and results
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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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.