Agent Community Extraction for 2D-RoboSoccer

  • Ravi Sankar Penta
  • Kamalakar Karlapalem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)


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.


Team Performance Successful Agent Agent Community Successful Interaction Activity Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ravi Sankar Penta
    • 1
  • Kamalakar Karlapalem
    • 1
  1. 1.Center for Data Engineering, International Institute of Information TechnologyHyderabad

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