Bayesian Classifiers Supported by Ranking for Decision Making in Robot Soccer

Since 1997, RoboCup organizes robotics competitions in order to disseminate and promote technological advancement worldwide. One of the platforms created by the RoboCup is robot soccer, which consists of disputes between two different teams of autonomous robotic agents who play soccer according to pre-established rules. In this scenario, several researches have already been conducted to find an efficient strategy to manage the players in a totally autonomous way. This paper presents a method based on a hierarchical control system called STP (Skills, Tactics and Plays) for decision making in robot soccer within the Small Size category. Our main goal is to apply Bayesian classifiers supported by ranking for choosing the appropriate Play (from STP model) to be performed according to the state of the game. We have evaluated the results of three Bayesian classifiers: Naive Bayes, TAN and K2. Empirical results obtained in the initial experiments indicate that the proposed method is promising, and it tends to be tolerant to classification errors.