Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2016, Article ID 7420984, 13 pages
http://dx.doi.org/10.1155/2016/7420984
Research Article

Active Player Modeling in the Iterated Prisoner’s Dilemma

Department of Computer Science and Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea

Received 12 November 2015; Revised 14 January 2016; Accepted 20 January 2016

Academic Editor: Reinoud Maex

Copyright © 2016 Hyunsoo Park and Kyung-Joong Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K.-J. Kim and S.-B. Cho, “Inference of other's internal neural models from active observation,” BioSystems, vol. 128, pp. 37–47, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. C. L. Baker, R. Saxe, and J. B. Tenenbaum, “Action understanding as inverse planning,” Cognition, vol. 113, no. 3, pp. 329–349, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. S. S. Farooq and K.-J. Kim, “Game player modeling,” in Encyclopedia of Computer Graphics and Games, pp. 1–15, Springer, 2016. View at Google Scholar
  4. G. Kendall, X. Yao, and S. Y. Chong, The Iterated Prisoners' Dilemma: 20 Years on, World Scientific Publishing, River Edge, NJ, USA, 2007.
  5. M. Gaudesi, E. Piccolo, G. Squillero, and A. Tonda, “TURAN: evolving non-deterministic players for the iterated prisoner's dilemma,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '14), pp. 21–27, Beijing, China, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, Philadelphia, Pa, USA, 2011.
  7. R. B. Marimont and M. B. Shapiro, “Nearest neighbour searches and the curse of dimensionality,” IMA Journal of Applied Mathematics, vol. 24, no. 1, pp. 59–70, 1979. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Settles, Active Learning Literature Survey, University of Wisconsin-Madison, 2010.
  9. H. S. Seung, M. Opper, and H. Sompolinsky, “Query by committee,” in Proceedings of the 5th Annual Workshop on Computational Learning Theory, pp. 287–294, New York, NY, USA, July 1992. View at Scopus
  10. J. C. Bongard and H. Lipson, “Nonlinear system identification using coevolution of models and tests,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 4, pp. 361–384, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. J. M. Houston, J. Kinnie, B. Lupo, C. Terry, and S. S. Ho, “Competitiveness and conflict behavior in simulation of a social dilemma,” Psychological Reports, vol. 86, no. 3, pp. 1219–1225, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Hemesath, “Cooperate or defect? Russian and American student in a prisoners dilemma,” Comparative Economic Studies, vol. 176, pp. 83–93, 1994. View at Google Scholar
  13. R. Axelrod and W. D. Hamilton, “The evolution of cooperation,” Science, vol. 211, no. 4489, pp. 1390–1396, 1981. View at Publisher · View at Google Scholar · View at MathSciNet
  14. M. Nowak and K. Sigmund, “A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game,” Nature, vol. 364, no. 6432, pp. 56–58, 1993. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Axelrod, “The evolution of strategies in the iterated prisoner's dilemma,” in Genetic Algorithms and Simulated Annealing, L. Davis, Ed., pp. 32–41, Morgan Kaufmann Publishers, London, UK, 1987. View at Google Scholar
  16. D. B. Fogel, “Evolving behaviors in the iterated prisoner's dilemma,” Evolutionary Computation, vol. 1, no. 1, pp. 77–97, 1993. View at Publisher · View at Google Scholar
  17. B. Beaufils, J.-P. Delahaye, and P. Mathieu, “Our meeting with gradual, a good strategy for the iterated prisoner's dilemma,” in Proceedings of the 5th International Workshop on the Synthesis and Simulation of Living Systems, pp. 202–209, Nara, Japan, July 1997.
  18. D. Van Bragt, C. Van Kemenade, and H. La Poutré, “The influence of evolutionary selection schemes on the iterated prisoner's dilemma,” Computational Economics, vol. 17, no. 2-3, pp. 253–263, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  19. D. Jang, P. A. Whigham, and G. Dick, “On evolving fixed pattern strategies for Iterated Prisoner's Dilemma,” in Proceedings of the 27th Australasian Conference on Computer Science (ACSC '04), vol. 26, pp. 241–247, Dunedin, New Zealand, January 2004.
  20. D. Ashlock, E.-Y. Kim, and N. Leahy, “Understanding representational sensitivity in the iterated prisoner's dilemma with fingerprints,” IEEE Transactions on Systems, Man and Cybernetics, Part C, vol. 36, no. 4, pp. 464–475, 2006. View at Publisher · View at Google Scholar
  21. H. Ishibuchi, H. Ohyanagi, and Y. Nojima, “Evolution of strategies with different representation schemes in a spatial iterated prisoner's dilemma game,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 3, no. 1, pp. 67–82, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Li, P. Hingston, and G. Kendall, “Engineering design of strategies for winning iterated prisoner's dilemma competitions,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 3, no. 4, pp. 348–360, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Gaudesi, E. Piccolo, G. Squillero, and A. Tonda, “Exploiting evolutionary modeling to prevail in iterated prisoner's dilemma tournaments,” IEEE Transactions on Computational Intelligence and AI in Games, 2015. View at Publisher · View at Google Scholar
  24. D. D. Lewis and W. A. Gale, “A sequential algorithm for training text classifiers,” in SIGIR '94: Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, organised by Dublin City University, pp. 3–12, Springer, London, UK, 1994. View at Publisher · View at Google Scholar
  25. F. Pedregosa, G. Varoquaux, A. Gramfort et al., “Scikit-learn: machine learning in python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. View at Google Scholar
  26. C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948. View at Publisher · View at Google Scholar · View at MathSciNet
  27. J. Wu and R. Axelrod, “How to cope with noise in the iterated prisoner's dilemma,” Journal of Conflict Resolution, vol. 39, no. 1, pp. 183–189, 1995. View at Publisher · View at Google Scholar
  28. J. Togelius, N. Shaker, and G. Y. Yannakakis, “Active player modeling,” in Proceedings of the 9th International Conference on the Foundations of Digital Games (FDG '14), April 2014.
  29. H. Park and K.-J. Kim, “Opponent modeling with incremental active learning: a case study of iterative prisoner's dilemma,” in Proceedings of the IEEE Conference on Computational Intelligence in Games (CIG '13), pp. 1–2, Niagara Falls, Canada, August 2013. View at Publisher · View at Google Scholar