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Rule-Based Behavior Prediction of Opponent Agents Using Robocup 3D Soccer Simulation League Logfiles

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Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 381)


Opponent modeling in games deals with analyzing opponents’ behavior and devising a winning strategy. In this paper we present an approach to model low level behavior of individual agents using Robocup Soccer Simulation 3D environment. In 2D League, the primitive actions of agents such as Kick, Turn and Dash are known and high level behaviors are derived using these low level behaviors. In 3D League, however, the problem is complex as actions are to be inferred by observing the game. Our approach, thus, serves as a middle tier in which we learn agent behavior by means of manual data tagging by an expert and then use the rules generated by the PART algorithm to predict opponent behavior. A parser has been written for extracting data from 3D logfiles, thus making our approach generalized. Experimental results on around 6000 records of 3D league matches show very promising results.


  • Robocup Soccer
  • PART algorithm
  • opponent modeling
  • machine learning


  1. Symeonidis, A., Mitkas, P.: A Methodology for Predicting Agent Behavior by the Use of Data Mining Techniques. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds.) AIS-ADM 2005. LNCS (LNAI), vol. 3505, pp. 161–174. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  2. Robocup official website,

  3. Nakashima, T., Ishibuchi, H.: Mimicking Dribble Trajectories by Neural Networks for RoboCup Soccer Simulation. In: IEEE 22nd International Symposium on Intelligent Control, ISIC 2007, pp. 658–663. IEEE (2007)

    Google Scholar 

  4. Nakashima, T., Uenishi, T., Narimoto, Y.: Off-line learning of soccer formations from game logs. In: World Automation Congress (WAC), pp. 1–6. IEEE (2010)

    Google Scholar 

  5. Faria, B.M., Reis, L.P., Lau, N., Castillo, G.: Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams. In: 2010 IEEE Conference on Cybernetics and Intelligent Systems (CIS), pp. 344–349. IEEE (2010)

    Google Scholar 

  6. Ball, D., Wyeth, G.: Classifying an opponents behavior in robot soccer. In: Proceedings of the Australasian Conference on Robotics and Automation, Australia (2003)

    Google Scholar 

  7. Ledezma, A., Aler, R., Sanchis, A., Borrajo, D.: OMBO: An opponent modeling approach. AI Communications 22(1), 21–35 (2009)

    MathSciNet  MATH  Google Scholar 

  8. 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)

    CrossRef  Google Scholar 

  9. Iglesias, J.A., Ledezma, A., Sanchis, A.: CAOS Coach 2006 Simulation Team: An Opponent Modelling Approach. Computing and Informatics Journal 28(1), 57–80 (2009)

    Google Scholar 

  10. Kuhlmann, G., Knox, W.B., Stone, P.: Know thine enemy: A champion RoboCup coach agent. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1463–1468 (2006)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  12. Robocup Simulation Coach Competition,

  13. Eibe, F., Witten, I.H.: Generating Accurate Rule Sets without Global Optimization. In: Proceedings of the 15th International Conference on Machine Learning, San Francisco, USA (1998)

    Google Scholar 

  14. Simspark,

  15. RoboViz official webiste,

  16. Holmes, G., Donkin, A., Witten, I.H.: WEKA: A Machine Learning Workbench. In: Proceedings of Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia (1994)

    Google Scholar 

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Larik, A.S., Haider, S. (2012). Rule-Based Behavior Prediction of Opponent Agents Using Robocup 3D Soccer Simulation League Logfiles. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds) Artificial Intelligence Applications and Innovations. AIAI 2012. IFIP Advances in Information and Communication Technology, vol 381. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33408-5

  • Online ISBN: 978-3-642-33409-2

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