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International Journal of Computer Games Technology
Volume 2011, Article ID 834026, 17 pages
http://dx.doi.org/10.1155/2011/834026
Research Article

Determining Solution Space Characteristics for Real-Time Strategy Games and Characterizing Winning Strategies

Department of Electrical and Computer Engineering, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright Patterson AFB, Dayton, OH 45433, USA

Received 24 September 2010; Revised 7 January 2011; Accepted 2 March 2011

Academic Editor: Alexander Pasko

Copyright © 2011 Kurt Weissgerber et al. 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. B. Geryk, A History of Real-Time Strategy Games, GameSpot, 2008.
  2. S. M. Lucas and G. Kendall, “Evolutionary computation and games,” IEEE Computational Intelligence Magazine, vol. 1, no. 1, pp. 10–18, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Ontañón, K. Mishra, N. Sugandh, and A. Ram, “Case-based planning and execution for real-time strategy games,” in Proceedings of the 7th International Conference on Case-Based Reasoning, vol. 4626 of Lecture Notes in Computer Science, pp. 164–178, Springer, Berlin, Germany, 2007. View at Google Scholar · View at Scopus
  4. D. W. Aha1, M. Molineaux, and M. Ponsen, “Learning to win: casebased plan selection in a RTS game,” in Proceedings of the 6th International Conference on Case-Based Reasoning (ICCBR '05), H. Muoz-Avila and F. Ricci, Eds., pp. 5–20, Springer, 2005.
  5. M. Sharma, M. Holmes, J. Santamaria, A. Irani, C. Isbell, and A. Ram, “Transfer learning in real-time strategy games using hybrid CBR/RL,” in International Joint Conference on Artificial Intelligence, 2007.
  6. T. Graepel, R. Herbrich, and J. Gold, “Learning to fight,” in Proceedings of Computer Games: Artificial Intelligence, Design and Education (CGAIDE '04), Q. Mehdi, N. Gough, and D. Al-Dabass, Eds., pp. 193–200, 2004.
  7. M. Molineaux, D. W. Aha, and P. Moore, “Learning continuous action models in a real-time strategy environment,” in Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference (FLAIRS '08), pp. 257–262, AAAI Press, May 2008. View at Scopus
  8. P. Spronck, M. Ponsen, I. Sprinkhuizen-Kuyper, and E. Postma, “Adaptive game AI with dynamic scripting,” Machine Learning, vol. 63, no. 3, pp. 217–248, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. E. Kok, Adaptive reinforcement learning agents in RTS games, M.S. thesis, University Utrecht, Utrecht, The Netherlands, 2008.
  10. M. Chung, M. Buro, and J. Schaeffer, “Monte Carlo planning in rts games,” in Proceedings of the IEEE Symposium on Computational Intelligence and Games, 2005.
  11. D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997. View at Google Scholar · View at Scopus
  12. F. Beerten, J. Salmon, L. Taulelle, F. Loeffler, N. Mistry, and T. Penfold, “Bos wars. Open Source Software,” 2008, http://www.boswars.org/.
  13. S. Bakkes, P. Kerbusch, P. Spronck, and J. van den Herik, “Automatically evaluating the status of an rts game,” in Proceedings of the Workshop on Reasoning, Representation, and Learning in Computer Games (IJCAI '05), 2005.
  14. R. Miikkulainen, B. D. Bryant, R. Cornelius, I. V. Karpov, K. O. Stanley, and C. H. Yong, “Computational intelligence in games,” in Computational Intelligence: Principles and Practice, G. Y. Yen and D. B. Fogel, Eds., IEEE Computational Intelligence Society, Piscataway, NJ, USA, 2006. View at Google Scholar
  15. A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artificial Intelligence, vol. 97, no. 1-2, pp. 245–271, 1997. View at Google Scholar · View at Scopus
  16. A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Computing Surveys, vol. 31, no. 3, pp. 316–323, 1999. View at Google Scholar · View at Scopus
  17. M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman, New York, NY, USA, 1979.
  18. P. Somol, P. Pudil, and J. Kittler, “Fast branch & bound algorithms for optimal feature selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 7, pp. 900–912, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Jarmulak and S. Craw, “Genetic algorithms for feature selection and weighting,” in Proceedings of the Workshop on Automating the Construction of Case Based Reasoners (IJCAI '99), 1999.
  20. R. Meiri and J. Zahavi, “Using simulated annealing to optimize the feature selection problem in marketing applications,” European Journal of Operational Research, vol. 171, no. 3, pp. 842–858, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983. View at Google Scholar · View at Scopus
  22. S. B. Kotsiantis, “Supervised machine learning: a review of classification techniques,” Informatica, vol. 31, no. 3, pp. 249–268, 2007. View at Google Scholar · View at Scopus
  23. A. Champandard, AI Game Development: Synthetic Creatures with Learning and Reactive Behaviors, New Riders, 2003.
  24. J. H. Friedman, “Regularized discriminant analysis,” Journal of the American Statistical Association, vol. 84, no. 405, pp. 165–175, 1989. View at Google Scholar
  25. B. Cestnik, I. Kononenko, and I. Bratko, “Assistant 86: a knowledgeelicitation tool for sophisticated users,” in Proceedings of the 2nd European Working Session on Learning, pp. 31–45, 1987. View at Google Scholar
  26. E. Larry Bull, Advances in Learning Classifier Systems, Springer, New York, NY, USA, 2004.
  27. R. L. de Mantaras and E. Armengol, “Machine learning from examples: inductive and lazy methods,” Data & Knowledge Engineering, vol. 25, no. 1-2, pp. 99–123, 1998. View at Google Scholar · View at Scopus
  28. E. E. Smith and D. Medin, Categories and Concepts, Harvard University Press, Cambridge, Mass, USA, 1981.
  29. S. Ridella, S. Rovetta, and R. Zunino, “K-winner machines for pattern classification,” IEEE Transactions on Neural Networks, vol. 12, no. 2, pp. 371–385, 2001. View at Publisher · View at Google Scholar · View at Scopus
  30. D. J. C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, Cambridge, UK, 2003.
  31. J. Pearl, Heuristics, Addison-Wesley, New York, NY, USA, 1984.
  32. F. J. Aherne, N. A. Thacker, and P. I. Rockett, “The Bhattacharyya metric as an absolute similarity measure for frequency coded data,” Kybernetika, vol. 34, no. 4, pp. 363–368, 1998. View at Google Scholar · View at Scopus
  33. E. Aarts and J. K. Lenstra, Local Seach in Combinatorial Optimization, Wiley, New York, NY, USA, 1997.
  34. K. Weissgerber, B. Borghetti, G. Lamont, and M. Mendenhall, “Towards automated feature selection in real time strategy games,” in GAMEON-NA Conference, August 2009.
  35. K. Weissgerber, B. J. Borghetti, and G. L. Peterson, “An effective and efficient real time strategy agent,” in Proceedings of the 23rd Annual Florida Artificial Intelligence Research Society Conference, 2010.
  36. S. Bakkes, P. Spronck, and J. van den Herik, “Phase-dependent evaluation in RTS games,” in Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence, pp. 3–10, 2007.