The UT Austin Villa 2003 Champion Simulator Coach: A Machine Learning Approach

  • Gregory Kuhlmann
  • Peter Stone
  • Justin Lallinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)


The UT Austin Villa 2003 simulated online soccer coach was a first time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating advice-giving as a machine learning problem. Competing against a field of mostly hand-coded coaches, the UT Austin Villa coach earned first place in the competition. In this paper, we present the multi-faceted learning strategy that our coach used and examine which aspects contributed most to the coach’s success.


Machine Learning Problem Uniform Number True Class Label Main Research Focus Player Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Chen, M., Foroughi, E., Heintz, F., Kapetanakis, S., Kostiadis, K., Kummeneje, J., Noda, I., Obst, O., Riley, P., Steffens, T., Wang, Y., Yin, X.: Users manual: RoboCup soccer server manual for soccer server version 7.07 and later (2003), Available at
  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: Robot Soccer World Cup VII. Springer, Berlin (2004) (to appear)Google Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  4. 4.
    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
  5. 5.
    Riley, P., Veloso, M.: On behavior classification in adversarial environments. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence, AAAI 2000 (2000)Google Scholar
  6. 6.
    Riley, P., Veloso, M.: Recognizing probabilistic opponent movement models. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, p. 453. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    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, vol. 5, pp. 215–224. Springer, Heidelberg (2002)Google Scholar
  8. 8.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (October 1999), Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Gregory Kuhlmann
    • 1
  • Peter Stone
    • 1
  • Justin Lallinger
    • 1
  1. 1.Department of Computer SciencesThe University of Texas at AustinAustin

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