Discovering Behavior Patterns in Muti-agent Teams

  • Fernando Ramos
  • Huberto Ayanegui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4953)


Three of the most important open research areas in multi-agent cooperative systems are the construction of models related with the communication between agents, the multiple interactions and the behaviors adopted by agents during a task [5] . This work deals with the discovery of behaviors in multi-agent teams, more precisely in soccer teams. It is an extension of a previous work presented in [1] . The extension is focused on the discovery of tactical plays adopted by soccer-agents during a match within the context of formations. Due to the nature of team work in soccer-agent domains, the discovery of tactical behaviors should be done within the context of team formations. Nevertheless, the dynamic nature and the multiple interactions between players at each instant of the game difficult the tracking of formations, which at the same time difficult the discovery of tactical plays. In this work is proposed an original and efficient way of discovering tactical plays supported by a robust tracking of formations, even though they are submitted to dynamic changes of the world, based on the construction of topological structures. Successful results, derived from the analysis of an important number of matches of the RoboCup Simulation league matches, valid the efficiency of the model presented in this work.


Soccer-agents behavior pattern recognition tactics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ayanegui, H., Ramos, F.: Recognizing patterns of dynamic behaviors based on multiple relations in soccer robotics domain. In: Ghosh, A., De Rajat, K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 33–40. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Berge, C.: Graphes. Guathier-Villars (1983)Google Scholar
  3. 3.
    Bezek, A., Gams, M., Bratko, I.: Multi-agent strategic modeling in a robotic soccer domain. In: Nakashima, H., Wellman, M.P., Weiss, G., Stone, P. (eds.) AAMAS, pp. 457–464. ACM Press, New York (2006)CrossRefGoogle Scholar
  4. 4.
    Devaney, M., Ram, A.: Needles in a haystack: Plan recognition in large spatial domains involving multiple agents. In: AAAI/IAAI, pp. 942–947 (1998)Google Scholar
  5. 5.
    Ferber, J.: Multi-Agent System: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman, Amsterdam (1999)Google Scholar
  6. 6.
    Freeman, H.: On the encoding of arbitrary geometric con?gurations. In: IRE Transactions, editor (1973)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Knuth, D.E.: The Art of Computer Programming. Addison-Wesley, Reading (1973)Google Scholar
  10. 10.
    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
  11. 11.
    Lattner, A.D., Miene, A., Visser, U., Herzog, O.: Sequential pattern mining for situation and behavior prediction in simulated robotic soccer. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 118–129. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Raines, T., Tambe, M., Marsella, S.: Automated assistants to aid humans in understanding team behaviors. In: Veloso, M.M., Pagello, E., Kitano, H. (eds.) RoboCup 1999. LNCS (LNAI), vol. 1856, pp. 85–102. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Visser, U., Drcker, C., Hbner, S., Schmidt, E., Weland, H.-G.: Recognizing formations in opponent teams. In: Stone, P., Balch, T.R., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 391–396. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fernando Ramos
    • 1
  • Huberto Ayanegui
    • 1
  1. 1.ITESM Campus CuernavacaTemixco MorelosMexico

Personalised recommendations