Abstract
The robot soccer game introduces a variable and dynamic environment for cooperating agents. Coverage of areas such as multi-agent systems, robot control, optimal path planning, real-time image processing and machine learning makes this domain very attractive. This article presents our approach to strategy description of the robot soccer game and a method of real-time strategy adaptation performed during the game. The real-time strategy adaptation method improves the strategy by adding new rules to it. During this process many new rules can be added to the original strategy, thus making it more robust but more difficult to manage. Therefore, this article presents our method for strategy reduction using representatives, in terms of the number of rules within the strategy, while preserving the quality of the adapted strategy. Strategy, as we defined it, describes a space from the real world in which we know the physical coordinates of objects located in it. Therefore, the methods we developed for strategy planning can be applied to it.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Osborne, M. J. (2004). An introduction to game theory. New York Oxford: Oxford University Press.
Kim, J.-H., Kim, D.-H., Kim, Y.-J., & Seow, K. T. (2010). Soccer robotics, Springer tracts in advanced robotics.
Ontanón, S., Mishra, K., Sugandh, N., & Ram, A. (2007). Case-based planning and execution for real-time strategy games. In Lecture Notes in Computer Science (pp. 164–178), Vol. 4626.
Huang, H. P., & Liang, C. C. (2002). Strategy-based decision making of a soccer robot system using a real-time self-organizing fuzzy decision tree. Fuzzy Sets and Systems, 127, 1.
Nakashima, T., Takatani, M., Udo, M., Ishibuchi, H., & Nii, M. (2006). Performance evaluation of an evolutionary method for robocup soccer strategies. In RoboCup 2005: Robot Soccer World Cup IX. Berlin: Springer.
Tominaga, M., Takemura, Y., & Ishii, K. (2017). Strategy analysis of robocup soccer teams using self-organizing map.
Chen, S., Lv, G., & Wang, X. (2016). Offensive strategy in the 2D soccer simulation league using multi-group ant colony optimization. International Journal of Advanced Robotic Systems, 13.
Larik, A. S. & Haider, S. (2016). On using evolutionary computation approach for strategy optimization in robot soccer. In 2nd International Conference on Robotics and Artificial Intelligence (ICRAI)
Akiyama, H., Tsuji, M., & Aramaki, S. (2016). Learning evaluation function for decision making of soccer agents using learning to rank. In Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems, 2016 Joint 8th International Conference on. IEEE.
Martinovič, J., Snášel, V., Ochodková, Zoltá, L., Wu, J., & Abraham, A. (2010). Robot soccer—Strategy description and game analysis. In Modelling and Simulation, 24th European Conference ECMS.
Svatoň, V., Martinovič, J., Slaninová, K., & Snášel, V. (2014). Improving rule selection from robot soccer strategy with substrategies. In Computer Information Systems and Industrial Management—13th IFIP TC8 International Conference (CISIM).
Dunham, M. H. (2003). In Data mining: Introductory and advanced topics. New Jersey: Prentice Hall.
Dráždilová, P., Martinovič, J., & Slaninová, K. (2013). Spectral clustering: Left-right-oscillate algorithm for detecting communities. In New Trends in Databases and Information Systems, Volume 185 of Advances in Intelligent Systems and Computing (pp. 285–294). Berlin, Heidelberg: Springer.
Klosgen, W., & Zytkow, J. M. (2002). Handbook of data mining and knowledge discovery. New York, NY, USA: Oxford University Press Inc.
Acknowledgements
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science—LQ1602”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Svatoň, V., Martinovič, J., Slaninová, K., Snášel, V. (2019). Robot Soccer Strategy Reduction by Representatives. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-1274-8_30
Download citation
DOI: https://doi.org/10.1007/978-981-13-1274-8_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1273-1
Online ISBN: 978-981-13-1274-8
eBook Packages: EngineeringEngineering (R0)