Abstract
Aiming at improving our physical strength and expanding our knowledge, tournaments and competitions have always contributed to our personal growth. Robotics and AI are no exception, and since beginning, competitions have been exploited to improve our understanding of such research areas (e.g. Chess, VideoGames, DARPA). In fact, the research community has launched (and it is involved) in several robotics competitions that provide a two-fold benefit of (i) promoting novel approaches and (ii) valuate proposed solutions systematically and quantitatively. In this paper, we focus on a particular research area of Robotics and AI: we analyze multi-robot systems deployed in a cooperative-adversarial environment being tasked to collaborate to achieve a common goal, while competing against an opposing team. To this end, RoboCup provide the best benchmarking environment by implementing such a challenging problem in the game of soccer. Sports, in fact, represent extremely complex challenge that require a team of robots to show dexterous and fluid movements and to feature high-level cognitive capabilities. Here, we analyse methodologies and approaches to address the problem of coordination and cooperation and we discuss state-of-the-art solutions that achieve effective decision-making processes for multi-robot adversarial scenarios.
V. Suriani and E. Antonioni—Contributed equally.
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References
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
Adachi, Y., Ito, M., Naruse, T.: Online strategy clustering based on action sequences in RoboCupSoccer small size league. Robotics 8(3), 58 (2019)
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
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
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)
Castelpietra, C., Iocchi, L., Nardi, D., Piaggio, M., Scalzo, A., Sgorbissa, A.: Communication and coordination among heterogeneous mid-size players: Art99. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) Robot Soccer World Cup. LNCS, vol. 2019, pp. 86–95. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45324-5_7
Catacora Ocana, J.M., Riccio, F., Capobianco, R., Nardi, D.: Cooperative multi-agent deep reinforcement learning in a 2 versus 2 free-kick task. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.A. (eds.) RoboCup 2019: Robot World Cup XXIII. LNCS, vol. 11531, pp. 44–57. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_4
Dorri, A., Kanhere, S.S., Jurdak, R.: Multi-agent systems: a survey. IEEE Access 6, 28573–28593 (2018). https://doi.org/10.1109/ACCESS.2018.2831228
Ghallab, M., Nau, D., Traverso, P.: Automated Planning and Acting. Cambridge University Press, Cambridge (2016)
Iglesias, J.A., Ledezma, A., Sanchis, A.: Opponent modeling in RoboCup Soccer simulation. In: Fuentetaja Pizán, R., García Olaya, Á., Sesmero Lorente, M.P., Iglesias Martínez, J.A., Ledezma Espino, A. (eds.) Advances in Physical Agents, vol. 855, pp. 303–316. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99885-5_21
Li, X., Chen, X.: Fuzzy inference based forecasting in soccer simulation 2D, the RoboCup 2015 soccer simulation 2D league champion team. In: Almeida, L., Ji, J., Steinbauer, G., Luke, S. (eds.) RoboCup 2015: Robot World Cup XIX. LNCS, vol. 9513, pp. 144–152. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-29339-4_12
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)
Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson Education, London (2005)
MacAlpine, P., Barrera, F., Stone, P.: Positioning to win: a dynamic role assignment and formation positioning system. In: Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)
Masterjohn, J.G., Polceanu, M., Jarrett, J., Seekircher, A., Buche, C., Visser, U.: Regression and mental models for decision making on robotic biped goalkeepers. In: Almeida, L., Ji, J., Steinbauer, G., Luke, S. (eds.) RoboCup 2015: Robot World Cup XIX. LNCS, vol. 9513, pp. 177–189. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-29339-4_15
Mendoza, J.P., Simmons, R., Veloso, M.: Online learning of robot soccer free kick plans using a bandit approach. In: Twenty-Sixth International Conference on Automated Planning and Scheduling (2016)
OpenAI: OpenAI five. https://blog.openai.com/openai-five/ (2018)
Pierson, H.A., Gashler, M.S.: Deep learning in robotics: a review of recent research. Adv. Robot. 31(16), 821–835 (2017)
Riccio, F., Borzi, E., Gemignani, G., Nardi, D.: Context-based coordination for a multi-robot soccer team. In: Almeida, L., Ji, J., Steinbauer, G., Luke, S. (eds.) RoboCup 2015: Robot World Cup XIX. LNCS, vol. 9513, pp. 276–289. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-29339-4_23
Riccio, F., Capobianco, R., Nardi, D.: Using Monte Carlo search with data aggregation to improve robot soccer policies. In: Behnke, S., Sheh, R., Sarıel, S., Lee, D.D. (eds.) RoboCup 2016: Robot World Cup XX. LNCS, vol. 9776, pp. 256–267. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68792-6_21
Risler, M., von Stryk, O.: Formal behavior specification of multi-robot systems using hierarchical state machines in XABSL. In: AAMAS08-Workshop on Formal Models and Methods for Multi-robot Systems, pp. 12–16. Citeseer (2008)
Rizzi, C., Johnson, C.G., Vargas, P.A.: Fear learning for flexible decision making in RoboCup: a discussion. In: Akiyama, H., Obst, O., Sammut, C., Tonidandel, F. (eds.) RoboCup 2017: Robot World Cup XXI. LNCS, vol. 1117, pp. 59–70. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00308-1_5
Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–503 (2016)
Spaan, M.T., Vlassis, N., Groen, F.C., et al.: High level coordination of agents based on multiagent Markov decision processes with roles. In: IROS, vol. 2, pp. 66–73 (2002)
Suzuki, Y., Nakashima, T.: On the use of simulated future information for evaluating game situations. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.A. (eds.) RoboCup 2019: Robot World Cup XXIII. LNCS, vol. 11531, pp. 294–308. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_23
Trevizan, F.W., Veloso, M.M.: Learning opponent’s strategies in the RoboCup small size league. In: Proceedings of the AAMAS, vol. 10. Citeseer (2010)
Watkinson, W.B., Camp, T.: Training a RoboCup striker agent via transferred reinforcement learning. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018: Robot World Cup XXII. LNCS, vol. 11374, pp. 109–121. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_9
Wurman, P.R., D’Andrea, R., Mountz, M.: Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag. 29(1), 9 (2008). https://doi.org/10.1609/aimag.v29i1.2082. https://ojs.aaai.org/index.php/aimagazine/article/view/2082
Yasui, K., Kobayashi, K., Murakami, K., Naruse, T.: Analyzing and learning an opponent’s strategies in the RoboCup small size league. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013: Robot World Cup XVII. LNCS, vol. 8371, pp. 159–170. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44468-9_15
Zhou, Z.H., Yu, Y., Qian, C.: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-5956-9
Ziparo, V.A., Iocchi, L., Nardi, D., Palamara, P.F., Costelha, H.: Petri net plans: a formal model for representation and execution of multi-robot plans. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 1, pp. 79–86. International Foundation for Autonomous Agents and Multiagent Systems (2008)
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Suriani, V., Antonioni, E., Riccio, F., Nardi, D. (2021). Coordination and Cooperation in Robot Soccer. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_16
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