Using Machine Learning approaches to detect opponent formation
Ehsan Asali, Mojtaba Valipour, Nader Zare, Ardavan Afshar, MohammadReza Katebzadeh, Gholamhossein Dastghaibyfard · 2016
Making a correct decision is a difficult task in a Soccer Simulation 2D environment due to the fact that there is a lack of information for each agent. Therefore, coach agent can take role as a mediator for agents to analyze data and inform players about crucial events by sending command messages. This paper proposes a new method to detect the formation of opponents which is not still possible for agents to extract. In the experimental results of this paper, we show that team formation is successfully learned by various well-known classification algorithms.