Using Machine Learning Approaches to Detect Opponent Formation

被引：0
|
作者：
Asali, Ehsan [1 ]
Valipour, Mojtaba [1 ]
Zare, Nader [2 ]
Afshar, Ardavan [1 ]
Katebzadeh, MohammadReza [1 ]
Dastghaibyfard, G. H. [1 ]
机构：
[1] Shiraz Univ, Dept Comp Sci & Engn & IT, Shiraz, Iran
[2] KN Toosi Univ Technol, Dept Comp & Elect Engn, Tehran, Iran
来源：
2016 ARTIFICIAL INTELLIGENCE AND ROBOTICS (IRANOPEN) | 2016年
关键词：
Formation Detection; Classification; Multi-Agent Systems; RoboCup; Soccer Simulation;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：140 / 144
页数：5
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