Dipartimento di Ingegneria informatica, automatica e gestionale

Fan input and support is an important component in many individual and team sports, ranging from athletics to basketball. Audience interaction provides a consistent impact on the athletes’ performance. The analysis of the crowd noise can provide a global indication on the ongoing game situation, less conditioned by subjective factors that can influence a single fan. In this work, we exploit the collective intelligence of the audience of a robot soccer match to improve the performance of the robot players. In particular, audio features extracted from the crowd noise are used in a Reinforcement Learning process to possibly modify the game strategy. The effectiveness of the proposed approach is demonstrated by experiments on registered crowd noise samples from several past RoboCup SPL matches.