An Overview on Opponent Modeling in RoboCup Soccer Simulation 2D

  • Shokoofeh Pourmehr
  • Chitra Dadkhah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)


This paper reviews the proposed opponent modeling algorithms within the soccer simulation domain. RoboCup soccer simulation 2D is a rich multi agent environment where opponent modeling plays a crucial role. In multi agent systems with adversarial and cooperative agents, team agents should be adapted to the current environment and opponent in order to propose appropriate and effective counteractions. Predicting the opponent’s future behaviors during competition allows for more informed decisions. We divide opponent modeling into two categories of individual agent behaviors and team behaviors. Individual behaviors concern modeling the low-level behaviors of individual opponent agents, however in team behaviors, the high-level strategy of the entire team like formation, offensive and defensive system, is recognized. Several methods have been proposed to create different models of opponents to improve the performance of teams in an essential aspect. In this paper, we review the approaches to the problem of opponent modeling published from 2000 to 2010.


Opponent Modeling Soccer Simulation 2D Robotic Soccer RoboCup Multi-agent system 


  1. 1.
    Turocy, T.L., Stengel, B.V.: Game Theory. CDAM Research Report LSE-CDAM (2001)Google Scholar
  2. 2.
    Ball, D., Wyeth, G.: Classifying an Opponent’s Behaviour in Robot Soccer. In: Proceedings of the 2003 Australasian Conference on Robotics and Automation, ACRA (2003)Google Scholar
  3. 3.
    Fathzadeh, R., Mokhtari, V., Kangavari, M.R.: Opponent Provocation and Behavior Classification: A Machine Learning Approach. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds.) RoboCup 2007. LNCS (LNAI), vol. 5001, pp. 540–547. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Iglesias, J.A., Ledezma, A., Sanchís, A.: A Comparing Method of Two Team Behaviours in the Simulation Coach Competition. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds.) MDAI 2006. LNCS (LNAI), vol. 3885, pp. 117–128. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Kitano, H., Tambe, M., Stone, P., Veloso, M., Coradeschi, S., Osawa, E., Matsubara, H., Noda, I., Asada, M.: The RoboCup Synthetic Agent Challenge 1997. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pp. 24–29 (1997)Google Scholar
  6. 6.
    Noda, I., Matsubara, H., Hiraki, K., Frank, I.: Soccer server: A tool for research on multi-agent systems. Applied Artificial Intelligence 12(2–3), 233–250 (1998)CrossRefGoogle Scholar
  7. 7.
    Nakashima, T., Uenishi, T., Narimoto, Y.: Off-line learning of soccer formations from game logs. In: World Automation Congress (WAC), pp. 1–6 (2010)Google Scholar
  8. 8.
    Marín, C.A., Castillo, L.P., Garrido, L.: Dynamic Adaptive Opponent Modeling: Predicting Opponent Motion while Playing Soccer. In: Eduardo Alonso, E., Guessoum, Z. (eds.) Fifth European Workshop on Adaptive Agents and Multi-agent Systems Proceedings. LIP6, Paris, France (March 2005)Google Scholar
  9. 9.
    Fyfe, C., Tiño, P., Charles, D., García-Osorio, C., Yin, H.: Intelligent Data Engineering and Automated Learning. In: IDEAL 2010. Springer (2010)Google Scholar
  10. 10.
    Laviers, K., Sukthankar, G., Klenk, M., Aha, D.W., Molineaux, M.: Opponent modeling and spatial similarity to retrieve and reuse superior plays. In: Proceedings of the Workshop on Case-Based Reasoning for Computer Games. AAAI Press, California (2009)Google Scholar
  11. 11.
  12. 12.
    Almeida, R., Reis, L.P., Jorge, A.M.: Analysis and Forecast of Team Formation in the Simulated Robotic Soccer Domain. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds.) EPIA 2009. LNCS (LNAI), vol. 5816, pp. 239–250. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Riley, P., Veloso, M.: On Behavior Classification in Adversarial Environments. In: Parker, L.E., Bekey, G., Barhen, J. (eds.) Distributed Autonomous Robotic Systems, vol, vol. 4, pp. 371–380. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Riley, P., Veloso, M.: Recognizing Probabilistic Opponent Movement Models. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 453–458. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Riley, P., Veloso, M.: Coaching a Simulated Soccer Team by Opponent Model Recognition. In: Proceedings of the Fifth International Conference on Autonomous Agents (Agents 2001), pp. 155–156 (2001)Google Scholar
  16. 16.
    Riley, P., Veloso, M.: Planning for distributed execution through use of probabilistic opponent models. In: Proceedings of the IJCAI-2001Workshop PRO-2: Planning under Uncertainty and Incomplete Information, pp. 72–81 (2001)Google Scholar
  17. 17.
    Veloso, M.M., Pollack, M.E., Cox, M.T.: Rationale-Based Monitoring for Planning in Dynamic Environments. In: Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems (1998)Google Scholar
  18. 18.
    Huang, Z., Yang, Y., Chen, X.: An approach to plan recognition and retrieval for multi-agent systems. In: Proc. of AORC, Sydney, Australia (January 2003)Google Scholar
  19. 19.
    Iglesias, J.A., Ledezma, A., Sanchis, A.: Caos coach 2006 simulation team: An opponent modeling approach. Computing and Informatics 28(1), 57–80 (2009)Google Scholar
  20. 20.
    Iglesias, J.A., Ledezma, A., Sanchis, A.: Comparing behavior in agent modeling task. Structure, 289–296 (2006)Google Scholar
  21. 21.
    Iglesias, J.A., Ledezma, A., Sanchis, A., Kaminka, G.A.: Classifying efficiently the behavior of a soccer team. In: Burgard, W., et al. (eds.) IAS-10, pp. 316–323 (2008)Google Scholar
  22. 22.
    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
  23. 23.
    Steffens, T.: Similarity-based opponent modeling using imperfect domain theories. In: CIG (2005)Google Scholar
  24. 24.
    Ahmadi, M., Lamjiri, A.K., Nevisi, M.M., Habibi, J., Badie, K.: Using a two-layered case-based reasoning for prediction in soccer coach. In: Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, pp. 181–185 (2004)Google Scholar
  25. 25.
    Kaminka, G.A., Fidanboylu, M., Chang, A., Veloso, 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
  26. 26.
    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
  27. 27.
    Ledezma, A., Aler, R., Sanchís, A., Borrajo, D.: Predicting Opponent Actions by Observation. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 286–296. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  28. 28.
    Ramos, F., Ayanegui, H.: Discovering Tactical Behavior Patterns Supported by Topological Structures in Soccer Agent Domains. In: International Conference on Autonomous Agents, Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, Estoril, vol. 3, pp. 1421–1424 (2008)Google Scholar
  29. 29.
    Fathzadeh, R., Mokhtari, V., Mousakhani, M., Mahmoudi, F.: Mining Opponent Behavior: A Champion of RoboCup Coach Competition. In: IEEE 3rd Latin American Robotics Symposium, pp. 80–83 (2006)Google Scholar
  30. 30.
    Fathzadeh, R., Mokhtari, V., Mousakhani, M., Shahri, A.M.: Coaching with Expert System Towards RoboCup Soccer Coach Simulation. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 488–495. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  31. 31.
    Fathzadeh, R., Mokhtari, V., Haghighat, A.T., Mousakhani, M.: Using expert system in robocup soccer coach simulation: An opponent modeling approach. In: Proceedings Second IEEE Latin-American Robotics Symposium, Sao luis-Maranhao, Brazil (2005)Google Scholar
  32. 32.
    Bombini, G., Di Mauro, N., Ferilli, S., Esposito, F.: Classifying Agent Behaviour through Relational Sequential Patterns. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds.) KES-AMSTA 2010. LNCS, vol. 6070, pp. 273–282. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  33. 33.
    Reis, L.P., Lopes, R., Mota, L., Lau, N.: Playmaker: Graphical Definition of Formations and Setplays. In: Information Systems and Technologies (CISTI), pp. 1–6 (2010)Google Scholar
  34. 34.
    Uenishi, T., Nakashima, T.: Team Description of opuCI 2D for RoboCup (2009)Google Scholar
  35. 35.
    Ayanegui-Santiago, H.: Recognizing Team Formations in Multi-agent Systems: Applications in Robotic Soccer. In: Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, pp. 163–173 (2009)Google Scholar
  36. 36.
    Faria, B.M., Reis, L.P., Lau, N., Castillo, G.: Machine Learning Algorithms applied to the Classification of Robotic Soccer Formations and Opponent Team. In: Proceedings of the 2010 IEEE Conference on Cybernetics and Intelligent Systems (CIS) and Robotics, Automation and Mechatronics (RAM), Singapore, pp. 344–349 (2010)Google Scholar
  37. 37.
    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
  38. 38.
    Riley, P., Veloso, M., Kaminka, G.: An empirical study of coaching. In: Asama, H., Arai, T., Fukuda, T., Hasegawa, T. (eds.) Distributed Autonomous Robotic Systems 5, pp. 215–224. Springer (2002)Google Scholar
  39. 39.
    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
  40. 40.
    Weka. Weka Machine Learning Project, (acessed: October 04, 2008)
  41. 41.
    Stone, P., Riley, P., Veloso, M.: Defining and Using Ideal Teammate and Opponent Agent Models. In: Proceedings of the Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (2000)Google Scholar
  42. 42.
    Ledezma, A., Aler, R., Sanchis, A., Borrajo, D.: Predicting opponent actions in the RoboSoccer. In: IEEE International Conference on Systems, Man and Cybernetics, p. 5 (2002)Google Scholar
  43. 43.
    Ledezma, A., Aler, R., Sanchis, A., Borrajo, D.: OMBO: An opponent modeling approach. AI Communications 22, 21–35 (2009)MathSciNetzbMATHGoogle Scholar
  44. 44.
    Illobre, A., Gonzalez, J., Otero, R., Santos, J.: Learning action descriptions of opponent behavior in the Robocup 2D simulation environment. ILP (2010)Google Scholar
  45. 45.
    Chen, M., Foroughi, E., Heintz, S., Kapetanakis, S., Kostiadis, K., Kummeneje, J., Noda, I., Obst, O., Riley, P., Steffens, T., Wang, Y., Yin, X.: RoboCup Soccer Server manual for Soccer Server version 7.07 or Latest., (accessed on: October 01, (2003)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shokoofeh Pourmehr
    • 1
  • Chitra Dadkhah
    • 1
  1. 1.Computer and Electrical Engineering DepartmentK.N. Toosi University of TechnologyIran

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