Counter attack detection with machine learning from log files of RoboCup simulation
暂无分享,去创建一个
Multi-agent systems have attracted a lot of attention recently. RoboCup Soccer Simulation is treated here as a testbed of such systems. This study aims to facilitate the analysis of team behavior and to clarify the role of different types of team possession in the game results of RoboCup Soccer Simulation. We construct a method of detecting counter attacks, which are one type of team possession, by analyzing the log files of games. To detecting the counter attacks, anisotropy feature and others are introduced. Based on these features, a support vector machine (SVM) based detector was able to achieve a 77% detection rate. The detecting method will be expected to reduce the burden of visually checking log data.
[1] G. Parisi,et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study , 2007, Proceedings of the National Academy of Sciences.
[2] R. Bahr,et al. Developing a New Method for Team Match Performance Analysis in Professional Soccer and Testing its Reliability , 2009 .
[3] Huberto Ayanegui-Santiago. Recognizing Team Formations in Multiagent Systems: Applications in Robotic Soccer , 2009, ICCCI.