Skip to main content

Coordination and Cooperation in Robot Soccer

  • Conference paper
  • First Online:
Computational Collective Intelligence (ICCCI 2021)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Google Scholar 

  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)

    Google Scholar 

  6. 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

  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

  8. 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

  9. Ghallab, M., Nau, D., Traverso, P.: Automated Planning and Acting. Cambridge University Press, Cambridge (2016)

    Google Scholar 

  10. 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

  11. 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

  12. Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)

  13. Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson Education, London (2005)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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

  16. 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)

    Google Scholar 

  17. OpenAI: OpenAI five. https://blog.openai.com/openai-five/ (2018)

  18. Pierson, H.A., Gashler, M.S.: Deep learning in robotics: a review of recent research. Adv. Robot. 31(16), 821–835 (2017)

    Google Scholar 

  19. 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

  20. 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

  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)

    Google Scholar 

  22. 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

  23. Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–503 (2016)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. 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

  26. 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)

    Google Scholar 

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vincenzo Suriani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88081-1_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88080-4

  • Online ISBN: 978-3-030-88081-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics