Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Recognizing Formations in Opponent Teams
RoboCup 2000: Robot Soccer World Cup IV
FC Portugal Team Description: RoboCup 2000 Simulation League Champion
RoboCup 2000: Robot Soccer World Cup IV
Discovering tactical behavior patterns supported by topological structures in soccer agent domains
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
An overview on opponent modeling in RoboCup soccer simulation 2D
Robot Soccer World Cup XV
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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.