Coordination and Cooperation in Robot Soccer
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-88081-1_16
Reference31 articles.
1. Adachi, Y., Ito, M., Naruse, T.: Classifying the strategies of an opponent team based on a sequence of actions in the RoboCup SSL. In: Behnke, S., Sheh, R., Sarıel, S., Lee, D.D. (eds.) RoboCup 2016: Robot World Cup XX. LNCS, vol. 9776, pp. 109–120. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68792-6_9
2. Adachi, Y., Ito, M., Naruse, T.: Online strategy clustering based on action sequences in RoboCupSoccer small size league. Robotics 8(3), 58 (2019)
3. Akiyama, H., Tsuji, M., Aramaki, S.: Learning evaluation function for decision making of soccer agents using learning to rank. In: 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), pp. 239–242 (2016). https://doi.org/10.1109/SCIS-ISIS.2016.0059
4. Antonioni, E., Suriani, V., Riccio, F., Nardi, D.: Game strategies for physical robot soccer players: a survey. IEEE Trans. Games 1 (2021). https://doi.org/10.1109/TG.2021.3075065
5. Bakkes, S.C., Spronck, P.H., Van Den Herik, H.J.: Opponent modelling for case-based adaptive game AI. Entertain. Comput. 1(1), 27–37 (2009)
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