Recognizing Patterns of Dynamic Behaviors Based on Multiple Relations in Soccer Robotics Domain

  • Huberto Ayanegui
  • Fernando Ramos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)


This work is focused on the recognition of team patterns represented by different formations played by a soccer team during a match. In the soccer domain, the recognition of formation patterns is difficult due to the dynamic and real time conditions of the environment as well as the multiple interactions among team mates. In this work, some of these multiple interactions are modeled as relations represented by a topological graph which is able to manage the dynamic changes of structures. Thus, the topological graph serves to recognize apparent changes of formations from real changes of them. The proposed model has been tested with different teams in different matches of the Robocup Simulation League. The results have shown that the model can recognize the different main formations used by a team during a match even the multiple changes of the players due to the dynamic nature of a match.


Pattern recognition robotic soccer formations dynamic behavior 


  1. 1.
    Kaminka, G., A., Fidanboylu, M., Allen, C., Veloso, M.: Learning the Sequential Coordinated Behavior of Teams from Observations. In: Proceedings of the RoboCup-2002 Symposium, Fukuoka, Japan (June 2002)Google Scholar
  2. 2.
    Kuhlmann, G., Stone, P., Lallinger, J.: The Champion UT Austin Villa 2003 Simulator Online Coach Team. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Ubbo, V., Christian, D., Sebastian, H., Esko, S.: Weland Hans-Georg: Recognizing Formations in Opponent Teams. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, Springer, Heidelberg (2001)Google Scholar
  4. 4.
    Patrick, R., Manuela, V., Gal, K.: An empirical study of coaching. In: Distribuited Autonomous Robotic Systems 6, Springer, Heidelberg (2002)Google Scholar
  5. 5.
    Crelle, A.L.: Sammlung mathematischer Aufstze und Bemerkungen, vol. 1. Maurer Berlin, p. 176 (1821)Google Scholar
  6. 6.
    Itsuki, N., lan, F.: Investigating the Complex with Virtual soccer. In: Heudin, J.-C. (ed.) VW 1998. LNCS (LNAI), vol. 1434, pp. 241–253. Springer, Heidelberg (1998)Google Scholar
  7. 7.
    Kitano, H., Tambe, M., Stone, P., Veloso, M., Coradeschi, S., Osawa, E., Matsubara, Noda, I., Asada, M.: The RoboCup Synthetic Agent Challenge 1997. In: Proceedings of IJCAI 1997, Nagoya, Japan (August 1997)Google Scholar
  8. 8.
    David, C., Shaul, M.: Incorporating Opponent Models into Adversary Search. In: Thirteenth National Conference on Artificial lntelligence, Portland Oregon, AAAI Press (1996)Google Scholar
  9. 9.
    Bo, Y., Qinghua, W.: Agent brigade in dynamic formation of robotic soccer. In: Proceedings of the 3rd World Congress on Intelligent Control and Automation, vol. 1, pp. 174–178 (2000)Google Scholar
  10. 10.
    MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, vol. 1, pp. 281–29. University of California Press (1967)Google Scholar
  11. 11.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Huberto Ayanegui
    • 1
  • Fernando Ramos
    • 1
  1. 1.ITESM Campus Cuernavaca, Reforma 182-A, Col Lomas De Cuernavaca, 62589, Temixco MorelosMexico

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