Team-Strategy Analysis with Fuzzy Inference in RoboCup Soccer

Abstract

In this paper, we propose an analysis method with fuzzy inference in order to improve the classification accuracy of team strategies in RoboCup soccer. It is currently difficult to quantitatively evaluate team strategies because there are no appropriate ways to represent game situations and also there are an intractable number of factors such as field states and tactics. Therefore, the performance of tactical analysis is not high enough to identify unknown teams. Because the kick probability distribution proposed in the previous works cannot consider the kick directions, this paper employs a kick direction distribution obtained by kernel density estimation using von-Mises distributions. In a series of computational experiments, a fuzzy inference system with the kick probability distribution as well as kick direction distribution in the antecedent is constructed from the RoboCup games. This paper evaluates the classification accuracy in order to investigate the performance of the proposed method.