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Recognizing Team Formations in Multiagent Systems: Applications in Robotic Soccer

  • Huberto Ayanegui-Santiago
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5796)

Abstract

The main purpose of this work is the recognition of soccer team formations by considering a dynamic structural analysis. Traditionally, the recognition of team formations is carried-out without taking into account an expressive representation of relations between players. This kind of approaches are not able to manage the constant changes occurring in the soccer domain, which results in an inefficient way of recognizing formations immerse in dynamic environments. It is presented in this work an efficient model to recognize formations based on a representation that takes into account multiple relations among defender, midfielder and forward players. The proposed model has been tested with different teams in off-line mode showing that it is able to recognize the different main formations used by a team during a match.

Keywords

Robotic soccer Multiagent modeling Team formations 

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References

  1. 1.
    Carmel, D., Markovitch, S.: Incorporating opponent models into adversary search. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Portland, OR (1996)Google Scholar
  2. 2.
    Kaminka, G.A., Fidanboylu, M., Chang, A., Veloso, M.M.: Learning the sequential coordinated behavior of teams from observations. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 111–125. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Kitano, H., Tambe, M., Stone, P., Veloso, M.M., Coradeschi, S., Osawa, E., Matsubara, H., Noda, I., Asada, M.: The robocup synthetic agent challenge 97. In: Kitano, H. (ed.) RoboCup 1997. LNCS, vol. 1395, pp. 62–73. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Kuhlmann, G., Stone, P., Lallinger, J.: The ut austin villa 2003 champion simulator coach: A machine learning approach. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 636–644. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Nair, R., Tambe, M., Marsella, S., Raines, T.: Automated assistants for analyzing team behaviors. Autonomous Agents and Multi-Agent Systems 8(1), 69–111 (2004)CrossRefGoogle Scholar
  6. 6.
    Noda, I., Frank, I.: Investigating the complex with virtual soccer. In: Heudin, J.-C. (ed.) VW 1998. LNCS (LNAI), vol. 1434, pp. 241–253. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Riley, P., Veloso, M., Kaminka, G.: An empirical study of coaching. In: Distributed Autonomous Robotic Systems, vol. 5, pp. 215–224. Springer, Heidelberg (2002)Google Scholar
  8. 8.
    Steffens, T.: Feature-based declarative opponent-modelling. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 125–136. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Sycara, K.: Multiagent Systems, 10(1):79–93 (1998)Google Scholar
  10. 10.
    Visser, U., Drücker, C., Hübner, S., Schmidt, E., Weland, H.-G.: Recognizing formations in opponent teams. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 391–396. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Huberto Ayanegui-Santiago
    • 1
  1. 1.Facultad de Ciencias Basicas, Ingenieria y TecnologiaUniversidad Autonoma de TlaxcalaApizacoMexico

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