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Analysis and Forecast of Team Formation in the Simulated Robotic Soccer Domain

  • Rui Almeida
  • Luís Paulo Reis
  • Alípio Mário Jorge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5816)

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.

Keywords

Data Mining Classification Weka Sequential Minimal Optimization Formation Detection Simulated Robotic Soccer 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rui Almeida
    • 1
  • Luís Paulo Reis
    • 2
    • 4
  • Alípio Mário Jorge
    • 3
    • 5
  1. 1.FEP - Faculty of Economics of the University of PortoPortugal
  2. 2.FEUP - Faculty of Engineering of the University of PortoPortugal
  3. 3.FCUP - Faculty of Science of the University of PortoPortugal
  4. 4.LIACC – Artificial Intelligence and Computer Science Lab.University of PortoPortugal
  5. 5.LIAAD – Artificial Intelligence and Decision Support Lab.INESC Porto L.A.Portugal

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