Team Classification with Tactical Analysis Using Fuzzy Inference in RoboCup Soccer

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