Agent Community Extraction for 2D-RoboSoccer

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

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blankenburg, B., Klusch, M.: On safe kernel stable coalition forming among agents. In: AAMAS (2004)Google Scholar
  2. 2.
    van Steen, M., Ogston, E., Overeinder, B.J., Brazier, F.M.T.: A method for decentralized clustering in large multi-agent systems. In: AAMAS, pp. 789–796 (2003)Google Scholar
  3. 3.
    Sangüesa, R., Pujol, J.M., Delgado, J.: Extracting reputation in multi agent systems by means of social network topology. In: AAMAS, pp. 467–474 (2002)Google Scholar
  4. 4.
    Karypis, G., Han, E., Kumar, V.: Chameleon: Hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999)CrossRefGoogle Scholar
  5. 5.
    Kok, J.R., Spaan, M.T.J., Vlassis, N.: Multi-robot decision making using coordination graphs. In: de Almeida, A.T., Nunes, U. (eds.) Proceedings of the 11th International Conference on Advanced Robotics, ICAR 2003, Coimbra, Portugal, pp. 1124–1129 (June 2003)Google Scholar
  6. 6.
    Page, L., Brin, S.: The pagerank citation ranking: Bringing order to the web. In: WWW (1999)Google Scholar
  7. 7.
    Shehory, O., Kraus, S.: A kernel-oriented model for coalition-formation in general environments: Implementation and results. In: AAAI/IAAI, vol. 1 (1996)Google Scholar
  8. 8.
    Truszkowski, W., Karlin, J.: A cybernetic approach to the modeling of agent communities. In: Klusch, M., Kerschberg, L. (eds.) CIA 2000. LNCS, vol. 1860, pp. 166–178. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Wünstel, M., Polani, D., Uthmann, T., Perl, J.: Behavior classification with self-organizing maps. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS, vol. 2019, pp. 108–118. Springer, Heidelberg (2001)CrossRefGoogle Scholar

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

Personalised recommendations