Acquisition of cooperative tactics by soccer agents with ability of prediction and learning

Abstract

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.