予測能力を持つサッカーエージェントによる協調戦術の獲得

概要

論文の詳細を見る
Designing soccer agents operating on the Soccer Server has became a standard problem in the multiagent domain, and this paper describes the soccer agents that can learn to make use of cooperative tactics. Considering the ways actual coaches of soccer enable their players learn to execute the soccer tactics, we developed a method of agents' learning to distinguish good tactics from not-so-good tactics. It is made up mainly of small practical tasks requiring a few agents, of acquisition of appropriate cognitive maps by decomposing the situations into grid information, and of optimization of total play by a kind of adaptive learning. Because the agents perceive the environment as a grid, they have a finite number of condition spaces and are able to predict the behavior of opponents by learning the conditonal probabilities. Each condition has its own utility learned in an evolutionary method.
人工知能学会,東京大学大学院総合文化研究科広域科学専攻,Department of Systems Science, The University of Tokyo,東京大学大学院情報学環・学際情報学府,Interfaculty Initiative in Information Studies, The University of Tokyoの論文
2001-11-01