Abstract
Analyzing opponent teams has been established within the simulation league for a number of years. However, most of the analyzing methods are only available off-line. Last year we introduced a new idea which uses a time series-based decision tree induction to generate rules on-line. This paper follows that idea and introduces the approach in detail. We implemented this approach as a library function and are therefore able to use on-line coaches of various teams in order to test the method. The tests are based on two ‘models’: (a) the behavior of a goalkeeper, and (b) the pass behavior of the opponent players. The approach generates propositional rules (first rules after 1000 cycles) which have to be pruned and interpreted in order to use this new knowledge for one’s own team. We discuss the outcome of the tests in detail and conclude that on-line learning despite of the lack of time is not only possible but can become an effective method for one’s own team.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Boronowsky, M.: Diskretisierung reellwertiger Attribute mit gemischten kontinuierlichen Gleichverteilungen und ihre Anwendung bei der zeitreihenbasierten Entscheidungsbauminduktion. PhD thesis, Department of Mathematics and Computer Science, University of Bremen, St. Augustin (2001)
Drücker, C., Hübner, S., Visser, U., Weland, H.-G.: “as time goes by” - using time series based decision tree induction to analyze the behaviour of opponent players. In: RoboCup 2001, Robot Soccer World Cup V, LNCS, Seattle, Washington. Springer-Verlag (2002) (in print)
Fayyad, U., Irani, K.: On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8, 87–102 (1992)
Murray, J.: My goal is my castle – die höheren fähigkeiten eines robocup-agenten am beispiel des torwarts (1999), http://www.uni-koblenz.de/ag-ki/ROBOCUP/PAPER/papers.html
Quinlan, J.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Raines, T., Tambe, M., Marsella, S.: Automated assistants to aid humans in understanding team behabiors. In: Veloso, M., Pagello, E., Kitano, H. (eds.) Proceedings of the Third International Workshop on Robocup 2000, Robot Soccer World Cup IV, Stockholm, Sweden. LNCS, vol. 1856, pp. 85–102. Springer, Heidelberg (1999)
Riley, P., Veloso, M.: Recognizing probabilistic opponent movement models. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, Springer, Heidelberg (2002) (in print)
Visser, U., Drücker, C., Hübner, S., Schmidt, E., Weland, H.-G.: Recognizing formations in opponent teams. In: Stone, P., Balch, T., Kraetschmar, G. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 391–396. Springer, Heidelberg (2001)
Wünstel, M., Polani, D., Uthmann, T., Perl, J.: Behavior classification with self-organizing maps. In: Stone, P., Balch, T., Kraetschmar, G. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 108–118. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Visser, U., Weland, HG. (2003). Using Online Learning to Analyze the Opponent’s Behavior. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds) RoboCup 2002: Robot Soccer World Cup VI. RoboCup 2002. Lecture Notes in Computer Science(), vol 2752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45135-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-45135-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40666-2
Online ISBN: 978-3-540-45135-8
eBook Packages: Springer Book Archive