Abstract
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 noiseare 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.
E. Antonioni and V. Suriani—These two authors contributed equally.
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Antonioni, E., Suriani, V., Solimando, F., Nardi, D., Bloisi, D.D. (2022). Learning from the Crowd: Improving the Decision Making Process in Robot Soccer Using the Audience Noise. In: Alami, R., Biswas, J., Cakmak, M., Obst, O. (eds) RoboCup 2021: Robot World Cup XXIV. RoboCup 2021. Lecture Notes in Computer Science(), vol 13132. Springer, Cham. https://doi.org/10.1007/978-3-030-98682-7_13
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