Abstract
This paper proposes a classification approach to identify the team’s formation (formation means the strategical layout of the players in the field) in the robotic soccer domain for the two dimensional (2D) simulation league. It is a tool for decision support that allows the coach to understand the strategy of the opponent. To reach that goal we employ Data Mining classification techniques. To understand the simulated robotic soccer domain we briefly describe the simulation system, some related work and the use of Data Mining techniques for the detection of formations. In order to perform a robotic soccer match with different formations we develop a way to configure the formations in a training base team (FC Portugal) and a data preparation process. The paper describes the base team and the test teams used and the respective configuration process. After the matches between test teams the data is subjected to a reduction process taking into account the players’ position in the field given the collective. In the modeling stage appropriate learning algorithms were selected. In the solution analysis, the error rate (% incorrectly classify instances) with the statistic test t-Student for paired samples were selected, as the evaluation measure. Experimental results show that it is possible to automatically identify the formations used by the base team (FC Portugal) in distinct matches against different opponents, using Data Mining techniques. The experimental results also show that the SMO (Sequential Minimal Optimization) learning algorithm has the best performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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, http://sourceforge.net/projects/sserver (acessed on: October 01, 2003)
Reis, L.P., Lau, N.: FC portugal team description: RoboCup 2000 simulation league champion. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 29–40. Springer, Berlin (2001)
Lau, N., Reis, L.P.: FC Portugal – High Level Coordination Methodologies in Soccer Robotics. In: Lima, P. (ed.) Robotic Soccer, p. 598. Itech Education and Publishing, Vienna (2007)
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)
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)
Pro Evolution Soccer 2008. Konami Digital Entertainment GmbH, Frankfurt (2008)
Weka. Weka Machine Learning Project, http://www.cs.waikato.ac.nz/~ml/index.html (acessed: October 04, 2008)
Witten, I.H., Eibe, F.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 2nd edn. Morgan Kaufmann, St. Louis (2005)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning – Data Mining, Inference and Prediction. Springer, New York (2002)
Maroco, J.: Análise Estatística, 3rd edn. Edições Sílabo, Lisboa (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Almeida, R., Reis, L.P., Jorge, A.M. (2009). Analysis and Forecast of Team Formation in the Simulated Robotic Soccer Domain. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds) Progress in Artificial Intelligence. EPIA 2009. Lecture Notes in Computer Science(), vol 5816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04686-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-04686-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04685-8
Online ISBN: 978-3-642-04686-5
eBook Packages: Computer ScienceComputer Science (R0)