Abstract
The ability by the simulated soccer player to make rational decisions about moving without ball is a critical factor of success. Here we limit our scope to the offensive situation, i.e. when the ball is controlled by own team, and propose a systematic method for determining the optimal player position. Existing methods for accomplishing this task do not systematically balance risks and rewards, as they are not Pareto optimal by design. This may result in overlooking good opportunities. One more shortcoming of these methods is over simplifications in predicting the situation on the field, which may lead to performance loss. We propose two new ideas to address these issues. Experiments demonstrate that this results in a substantial increase in the team performance.
Chapter PDF
References
Kyrylov, V.: Balancing Rewards, Risks, Costs, and Real-Time Constraints in the Ball Passing Algorithm for the Robotic Soccer. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006: Robot Soccer World Cup X. LNCS (LNAI), vol. 4434. Springer, Heidelberg (2007)
Stone, P., Veloso, M., Riley, P.: The CMUnited-98 Champion Simulator Team. In: Asada, M., Kitano, H. (eds.) RoboCup 1998. LNCS (LNAI), vol. 1604, pp. 61–76. Springer, Heidelberg (1999)
Reis, L.P., Lau, N., Oliveira, E.C.: Situation Based Strategic Positioning for Coordinating a Team of Homogeneous Agents. In: Hannebauer, M., Wendler, J., Pagello, E. (eds.) ECAI-WS 2000. LNCS (LNAI), vol. 2103. Springer, Heidelberg (2001)
De Boer, R., Kok, J.: The Incremental Development of a Synthetic Multi-Agent System: The UvA Trilearn 2001 Robotic Soccer Simulation Team. Master’s Thesis. University of Amsterdam (2002)
Kok, J., Spaan, M., Vlassis, N.: Multi-Robot Decision Making Using Coordination Graphs. In: Proceedings of the International Conference on Advanced Robotics (ICAR), Coimbra, Portugal, pp. 1124–1129 (June 2003)
Andou, T.: Refinement of Soccer Agents’ Positions Using Reinforcement Learning. In: Kitano, H. (ed.) RoboCup 1997. LNCS, vol. 1395, pp. 373–388. Springer, Heidelberg (1998)
Nakashima, T., Udo, M., Ishibuchi, H.: Acquiring the positioning skill in a soccer game using a fuzzy Q-learning. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, July 16-20, 2003, vol. 3, pp. 1488–1491 (2003)
Kalyanakrishnan, S., Liu, Y., Stone, P.: Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006: Robot Soccer World Cup X. LNCS (LNAI), vol. 4434. Springer, Heidelberg (to appear, 2007)
Beim, G.: Principles of Modern Soccer. Houghton Mifflin Company, Boston (1977)
Vogelsinger, H.: The Challenge of Soccer. Allyn and Bacon, Inc., Boston (1973)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Acaemic Publishers, Berlin (1998)
Zhan, Y.: Tao of Soccer: An Open Source project (2006), https://sourceforge.net/projects/soccer/
Fisher, R.A., Bennett, J.H.: Statistical Methods, Experimental Design, and Scientific Inference. Oxford University Press, Oxford (1990)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kyrylov, V., Razykov, S. (2008). Pareto-Optimal Offensive Player Positioning in Simulated Soccer. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds) RoboCup 2007: Robot Soccer World Cup XI. RoboCup 2007. Lecture Notes in Computer Science(), vol 5001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68847-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-68847-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68846-4
Online ISBN: 978-3-540-68847-1
eBook Packages: Computer ScienceComputer Science (R0)