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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2015: Current Approaches in Applied Artificial Intelligence pp 478–487Cite as

Opponent Classification in Robot Soccer

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9101))

Abstract

The paper presents an approach to perform post-hoc analysis of RoboCup Soccer Simulation 3D teams via log files of their matches and to learn a model to classify them not only as being strong, medium or weak but also through their game playing styles such as frequent kickers, frequent dribblers, heavy/lean attackers, etc. The learned model can then be used to further cluster teams to predict game style of similar opponents. We have applied the presented approach to 22 teams from RoboCup 2011 in a fully automated fashion and the results show the validity of our approach.

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References

  1. Borghetti, B.J.: Opponent Modeling in Interesting Adversarial Environments. ProQuest (2008)

    Google Scholar 

  2. Schadd, F., Bakkes, E., Spronck, P.: Opponent modeling in real-time strategy games. In: Proceedings of the GAME-ON 2007, pp. 61–68 (2007)

    Google Scholar 

  3. Bjarnason, R.V., Peterson, T.S.: Multi-agent learning via implicit opponent modeling. In: Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC '02, vol. 2, pp. 1534–1539 (2002)

    Google Scholar 

  4. Julia Frolova, “Review of Multi-Agent Systems and Applications,” 29-Mar-2005. [Online]. http://jasss.soc.surrey.ac.uk/8/2/reviews/frolova.html. [accessed: 29-Mar-2013]

  5. Kitano, H., Tambe, M., Stone, P., Veloso, M., Coradeschi, S., Osawa, E., Matsubara, H., Noda, I., Asada, M.: The RoboCup synthetic agent challenge 97. In: RoboCup-97: Robot Soccer World Cup I, vol. 1395, H. Kitano, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 62–73 (1998)

    Google Scholar 

  6. “Robocup official website (www.robocup.org).”

  7. Larik, A.S., Haider, S.: Rule-based behavior prediction of opponent agents using robocup 3D soccer simulation league logfiles. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 381, pp. 285–295. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  8. MacAlpine, P., Collins, N., Lopez-Mobilia, A., Stone, P.: UT Austin Villa: RoboCup 2012 3D simulation league champion. In: Chen, X., Stone, P., Sucar, L.E., van der Zant, T. (eds.) RoboCup 2012. LNCS, vol. 7500, pp. 77–88. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  9. Pourmehr, S., Dadkhah, C.: An overview on opponent modeling in RoboCup soccer simulation 2D. In: Röfer, T., Mayer, N., Savage, J., Saranlı, U. (eds.) RoboCup 2011. LNCS, vol. 7416, pp. 402–414. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  10. Ledezma, A., Aler, R., Sanchis, A., Borrajo, D.: OMBO: An opponent modeling approach. AI Commun. 22121–35 IOS Press (2009)

    Google Scholar 

  11. Warden, T., Visser, U.: Real-time spatio-temporal analysis of dynamic scenes. Knowl. Inf. Syst. 32(2), 243–279 (2011)

    Article  Google Scholar 

  12. Riley, P.: Coaching: Learning and Using Environment and Agent Models for Advice. Carnegie Mellon University, (2005)

    Google Scholar 

  13. Nardi, D.: The UT Austin Villa 2003 Champion Simulator Coach: A Machine Learning Approach

    Google Scholar 

  14. Almeida, F., Abreu, P.H., Lau, N., Reis, L.P.: An automatic approach to extract goal plans from soccer simulated matches. Soft Comput. 17(5), 835–848 (2012)

    Article  Google Scholar 

  15. Erdogan, C., Veloso, M.: Action selection via learning behavior patterns in multi robot domains. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume One, Barcelona, Catalonia, Spain, pp. 192–197 (2011)

    Google Scholar 

  16. 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. LNCS, vol. 8371, pp. 159–170. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  17. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2009)

    Google Scholar 

  18. “Weka Software.” [Online]. Available: http://www.cs.waikato.ac.nz/ml/weka/

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Author information

Authors and Affiliations

  1. Artificial Intelligence Lab, Faculty of Computer Science, Institute of Business Administration, Garden Road, Karachi, 74400, Pakistan

    Asma S. Larik & Sajjad Haider

Authors
  1. Asma S. Larik
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  2. Sajjad Haider
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Corresponding author

Correspondence to Sajjad Haider .

Editor information

Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Moonis Ali

  2. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Young Sig Kwon

  3. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Chang-Hwan Lee

  4. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Juntae Kim

  5. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Yongdai Kim

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Larik, A.S., Haider, S. (2015). Opponent Classification in Robot Soccer. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_46

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  • DOI: https://doi.org/10.1007/978-3-319-19066-2_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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