Advertisement

Disruptive Innovations in RoboCup 2D Soccer Simulation League: From Cyberoos’98 to Gliders2016

  • Mikhail Prokopenko
  • Peter Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9776)

Abstract

We review disruptive innovations introduced in the RoboCup 2D Soccer Simulation League over the twenty years since its inception, and trace the progress of our champion team (Gliders). We conjecture that the League has been developing as an ecosystem shaped by diverse approaches taken by participating teams, increasing in its overall complexity. A common feature is that different champion teams succeeded in finding a way to decompose the enormous search-space of possible single and multi-agent behaviours, by automating the exploration of the problem space with various techniques which accelerated the software development efforts. These methods included interactive debugging, machine learning, automated planning, and opponent modelling. The winning approach developed by Gliders is centred on human-based evolutionary computation which optimised several components such as an action-dependent evaluation function, dynamic tactics with Voronoi diagrams, information dynamics, and bio-inspired collective behaviour.

Notes

Acknowledgments

Several people contributed to Cyberoos and Gliders development over the years. Marc Butler, Thomas Howard and Ryszard Kowalczyk made exceptionally valuable contributions to Cyberoos’ effort during 1998–2002 [9, 19, 20, 21]. We are grateful to Gliders team members Oliver Obst, particularly for establishing the tournament infrastructure supporting the team’s performance evaluation on CSIRO Accelerator Cluster (Bragg), and Victor Jauregui, for several important insights on soccer tactics used in Gliders2016 [37]. We thank David Budden for developing a new self-localisation method introduced in Gliders2013 [34, 49] as well as contributing to the analysis of competition formats [4], and Oliver Cliff for developing a new communication scheme adopted by Gliders from 2014 [35]. The overall effort has also benefited from the study quantifying tactical interaction networks, carried out in collaboration with Cliff et al. [46]. We are thankful to Ivan Duong, Edward Moore and Jason Held for their contribution to Gliders2012 [33]. Gliders team logo was created by Matthew Chadwick.

References

  1. 1.
    Asada, M., Kitano, H., Noda, I., Veloso, M.: RoboCup: today and tomorrow - what we have have learned. Artif. Intell. 110, 193–214 (1999)CrossRefGoogle Scholar
  2. 2.
    Kitano, H., Tambe, M., Stone, P., Veloso, M.M., Coradeschi, S., Osawa, E., Matsubara, H., Noda, I., Asada, M.: The RoboCup synthetic agent challenge 97. In: Kitano, H. (ed.) RoboCup 1997. LNCS, vol. 1395, pp. 62–73. Springer, Heidelberg (1998).  https://doi.org/10.1007/3-540-64473-3_49CrossRefGoogle Scholar
  3. 3.
    Budden, D., Wang, P., Obst, O., Prokopenko, M.: Simulation leagues: analysis of competition formats. In: Bianchi, R.A.C., Akin, H.L., Ramamoorthy, S., Sugiura, K. (eds.) RoboCup 2014. LNCS (LNAI), vol. 8992, pp. 183–194. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18615-3_15CrossRefGoogle Scholar
  4. 4.
    Budden, D.M., Wang, P., Obst, O., Prokopenko, M.: Robocup simulation leagues: enabling replicable and robust investigation of complex robotic systems. IEEE Robot. Autom. Mag. 22(3), 140–146 (2015)CrossRefGoogle Scholar
  5. 5.
    Noda, I., Stone, P.: The RoboCup soccer server and CMUnited clients: implemented infrastructure for MAS research. Auton. Agent. Multi-Agent Syst. 7(1–2), 101–120 (2003)CrossRefGoogle Scholar
  6. 6.
    Haker, M., Meyer, A., Polani, D., Martinetz, T.: A method for incorporation of new evidence to improve world state estimation. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 362–367. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45603-1_44CrossRefzbMATHGoogle Scholar
  7. 7.
    Stone, P., Veloso, M.: Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork. Artif. Intell. 110(2), 241–273 (1999)CrossRefGoogle Scholar
  8. 8.
    Riley, P., Stone, P., Veloso, M.: Layered disclosure: revealing agents’ internals. In: Castelfranchi, C., Lespérance, Y. (eds.) ATAL 2000. LNCS, vol. 1986, pp. 61–72. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44631-1_5CrossRefzbMATHGoogle Scholar
  9. 9.
    Butler, M., Prokopenko, M., Howard, T.: Flexible synchronisation within RoboCup environment: a comparative analysis. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) RoboCup 2000. LNCS, vol. 2019, pp. 119–128. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45324-5_10CrossRefGoogle Scholar
  10. 10.
    Stone, P., Riley, P., Veloso, M.: Defining and using ideal teammate and opponent models. In: Proceedings of the Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (2000)Google Scholar
  11. 11.
    Reis, L.P., Lau, N., Oliveira, E.C.: Situation based strategic positioning for coordinating a team of homogeneous agents. BRSDMAS 2000. LNCS, vol. 2103, pp. 175–197. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44568-4_11CrossRefGoogle Scholar
  12. 12.
    Noda, I., Suzuki, S., Matsubara, H., Asada, M., Kitano, H.: Robocup-97: the first robot world cup soccer games and conferences. AI Mag. 19(3), 49–59 (1998)Google Scholar
  13. 13.
    Stone, P., Riley, P., Veloso, M.: The CMUnited-99 champion simulator team. In: Veloso, M., Pagello, E., Kitano, H. (eds.) RoboCup 1999. LNCS (LNAI), vol. 1856, pp. 35–48. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-45327-X_2CrossRefGoogle Scholar
  14. 14.
    Stone, P., Asada, M., Balch, T., Fujita, M., Kraetzschmar, G., Lund, H., Scerri, P., Tadokoro, S., Wyeth, G.: Overview of Robocup-2000. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) RoboCup 2000. LNCS, vol. 2019, pp. 1–29. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45324-5_1CrossRefzbMATHGoogle Scholar
  15. 15.
    Reis, L.P., Lau, N.: FC Portugal team description: RoboCup 2000 simulation league champion. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) RoboCup 2000. LNCS, vol. 2019, pp. 29–40. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45324-5_2CrossRefGoogle Scholar
  16. 16.
    Reis, L.P., Lau, N.: COACH UNILANG - a standard language for coaching a (Robo) soccer team. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS, vol. 2377, pp. 183–192. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45603-1_19CrossRefGoogle Scholar
  17. 17.
    Jinyi, Y., Ni, L., Fan, Y., Yunpeng, C., Zengqi, S.: Technical solutions of tsinghuaeolus for Robotic soccer. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 205–213. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-25940-4_18CrossRefGoogle Scholar
  18. 18.
    Kok, J.R., Vlassis, N., Groen, F.: UvA Trilearn 2003 team description. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) CD RoboCup 2003. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Prokopenko, M., Kowalczyk, R., Lee, M., Wong, W.Y.: Designing and modelling situated agents systematically: Cyberoos98. In: Proceedings of the PRICAI-98 Workshop on RoboCup, pp. 75–89 (1998)Google Scholar
  20. 20.
    Prokopenko, M., Butler, M., Howard, T.: On emergence of scalable tactical and strategic behaviour. In: Stone, P., Balch, T., Kraetzschmar, G. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 357–366. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-45324-5_39CrossRefGoogle Scholar
  21. 21.
    Prokopenko, M., Wang, P., Howard, T.: Cyberoos 2001: Deep behaviour projection agent architecture. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS, vol. 2377, pp. 507–510. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45603-1_70CrossRefGoogle Scholar
  22. 22.
    Prokopenko, M., Wang, P.: Relating the Entropy of joint beliefs to multi-agent coordination. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 367–374. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-45135-8_32CrossRefGoogle Scholar
  23. 23.
    Prokopenko, M., Wang, P.: Evaluating team performance at the edge of chaos. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 89–101. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-25940-4_8CrossRefGoogle Scholar
  24. 24.
    Nehaniv, C., Polani, D., Olsson, L., Klyubin, A.: Evolutionary information-theoretic foundations of sensory ecology: channels of organism-specific meaningful information. In: da Fontoura Costa, L., Müller, G.B. (eds.) The 10th Altenberg Workshop in Theoretical Biology 2004 - Modeling Biology: Structures, Behavior, Evolution, Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria, pp. 9–11 (2005)Google Scholar
  25. 25.
    Prokopenko, M., Gerasimov, V., Tanev, I.: Measuring spatiotemporal coordination in a modular robotic system. In: Rocha, L., Yaeger, L., Bedau, M., Floreano, D., Goldstone, R., Vespignani, A., (eds.) Artificial Life X: Proceedings of The 10th International Conference on the Simulation and Synthesis of Living Systems, Bloomington IN, USA, pp. 185–191 (2006)Google Scholar
  26. 26.
    Prokopenko, M., Gerasimov, V., Tanev, I.: Evolving Spatiotemporal coordination in a modular robotic system. In: Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J.C.T., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D. (eds.) SAB 2006. LNCS (LNAI), vol. 4095, pp. 558–569. Springer, Heidelberg (2006).  https://doi.org/10.1007/11840541_46CrossRefGoogle Scholar
  27. 27.
    Riedmiller, M., Gabel, T., Trost, F., Schwegmann, T.: Brainstormers 2D - team description 2008. In: RoboCup 2008: Robot Soccer World Cup XII; CD (2008)Google Scholar
  28. 28.
    Zhang, H., Chen, X.: The decision-making framework of WrightEagle, the RoboCup 2013 Soccer simulation 2D league champion team. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS, vol. 8371, pp. 114–124. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_11CrossRefGoogle Scholar
  29. 29.
    Akiyama, H., Noda, I.: Multi-agent positioning mechanism in the dynamic environment. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds.) RoboCup 2007. LNCS (LNAI), vol. 5001, pp. 377–384. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68847-1_38CrossRefGoogle Scholar
  30. 30.
    Akiyama, H., Shimora, H.: Helios 2010 team description. In: RoboCup 2010: Robot Soccer World Cup XIV; CD (2010)Google Scholar
  31. 31.
    Chew, L.P.: Constrained delaunay triangulations. Algorithmica 4(1–4), 97–108 (1989)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Akiyama, H.: Agent2D Base Code (2010). http://www.rctools.sourceforge.jp
  33. 33.
    Prokopenko, M., Obst, O., Wang, P., Held, J.: Gliders 2012: tactics with action-dependent evaluation functions. In: RoboCup 2012 Symposium and Competitions: Team Description Papers, Mexico City, Mexico, June 2012 (2012)Google Scholar
  34. 34.
    Prokopenko, M., Obst, O., Wang, P., Budden, D., Cliff, O.: Gliders 2013: tactical analysis with information dynamics. In: RoboCup 2013 Symposium and Competitions: Team Description Papers, Eindhoven, The Netherlands, June 2013 (2013)Google Scholar
  35. 35.
    Prokopenko, M., Obst, O., Wang, P.: Gliders 2014: dynamic tactics with Voronoi diagrams. In: RoboCup 2014 Symposium and Competitions: Team Description Papers, Joao Pessoa, Brazil, July 2014 (2014)Google Scholar
  36. 36.
    Prokopenko, M., Wang, P., Obst, O.: Gliders 2015: opponent avoidance with bio-inspired flocking behaviour. In: RoboCup 2015 Symposium and Competitions: Team Description Papers, Hefei, China, July 2015 (2015)Google Scholar
  37. 37.
    Prokopenko, M., Wang, P., Obst, O., Jaurgeui, V.: Gliders 2016: integrating multi-agent approaches to tactical diversity. In: RoboCup 2016 Symposium and Competitions: Team Description Papers, Leipzig, Germany, July 2016 (2016)Google Scholar
  38. 38.
    Tavafi, A., Nozari, N., Vatani, R., Yousefi, M.R., Rahmatinia, S., Pirdir, P.: MarliK 2012 soccer 2D simulation team description paper. In: RoboCup 2012 Symposium and Competitions: Team Description Papers, Mexico City, Mexico (2012)Google Scholar
  39. 39.
    Kosorukoff, A.: Human based genetic algorithm. In: 2001 IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 3464–3469. IEEE (2001)Google Scholar
  40. 40.
    Cheng, C.D., Kosorukoff, A.: Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3102, pp. 983–993. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24854-5_98CrossRefGoogle Scholar
  41. 41.
    Tanev, I., Yuta, K.: Epigenetic programming: genetic programming incorporating epigenetic learning through modification of histones. Inf. Sci. 178(23), 4469–4481 (2008)CrossRefGoogle Scholar
  42. 42.
    Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: Information modification and particle collisions in distributed computation. Chaos 20(3), 037109 (2010)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Wang, X.R., Miller, J.M., Lizier, J.T., Prokopenko, M., Rossi, L.F.: Quantifying and tracing information cascades in swarms. PLoS One 7(7), e40084 (2012)CrossRefGoogle Scholar
  44. 44.
    Ay, N., Bernigau, H., Der, R., Prokopenko, M.: Information-driven self-organization: the dynamical system approach to autonomous robot behavior. Theor. Biosci. 131, 161–179 (2012)CrossRefGoogle Scholar
  45. 45.
    Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: Coherent information structure in complex computation. Theor. Biosci. 131, 193–203 (2012)CrossRefGoogle Scholar
  46. 46.
    Cliff, O.M., Lizier, J.T., Wang, X.R., Wang, P., Obst, O., Prokopenko, M.: Towards quantifying interaction networks in a football match. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS (LNAI), vol. 8371, pp. 1–12. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_1CrossRefGoogle Scholar
  47. 47.
    Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: A framework for the local information dynamics of distributed computation in complex systems. In: Prokopenko, M. (ed.) Guided Self-Organization: Inception. ECC, vol. 9, pp. 115–158. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-53734-9_5CrossRefGoogle Scholar
  48. 48.
    Luke, S.: Genetic programming produced competitive soccer softbot teams for RoboCup 97. In: Koza, J.R., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M.H., Goldberg, D.E., Iba, H., Riolo, R.L., (eds.) Proceedings of the 3rd Annual Genetic Programming Conference, Morgan Kaufmann, pp. 214–222 (1998)Google Scholar
  49. 49.
    Budden, D., Prokopenko, M.: Improved particle filtering for pseudo-uniform belief distributions in robot localisation. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds.) RoboCup 2013. LNCS (LNAI), vol. 8371, pp. 385–395. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-44468-9_34CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Complex Systems Research Group, Faculty of Engineering and ITThe University of SydneySydneyAustralia
  2. 2.Data MiningCSIRO Data61EppingAustralia

Personalised recommendations