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Evolving small-board Go players using coevolutionary temporal difference learning with archives Cover

Evolving small-board Go players using coevolutionary temporal difference learning with archives

Open Access
|Dec 2011

References

  1. Angeline, P. J. and Pollack, J. B. (1993). Competitive environments evolve better solutions for complex tasks,, Vol. 270, pp. 264-270.
  2. Azaria, Y. and Sipper, M. (2005). GP-Gammon: Genetically programming backgammon players,(3): 283-300.
  3. Bouzy, B. and Cazenave, T. (2001). Computer Go: An AI oriented survey,(1): 39-103.
  4. Bozulich, R. (1992)., Ishi Press, Tokyo.
  5. Bucci, A. (2007)., Ph.D. thesis, Brandeis University, Waltham, MA.
  6. Caverlee, J. B. (2000). A genetic algorithm approach to discovering an optimal blackjack strategy,, Stanford Book-store, Stanford, CA, pp. 70-79.
  7. de Jong, E. D. (2005). The MaxSolve algorithm for coevolution,, pp. 483-489.
  8. de Jong, E. D. (2007). A monotonic archive for paretocoevolution,(1): 61-93.
  9. Ficici, S. G. (2004)., Ph.D. thesis, Brandeis University, Waltham, MA.
  10. Ficici, S. and Pollack, J. (2003). A game-theoretic memory mechanism for coevolution,, pp. 286-297.
  11. Fogel, D. B. (2002)., San Francisco, CA.
  12. Hauptman, A. and Sipper, M. (2007). Evolution of an efficient search algorithm for the mate-in-n problem in chess,, pp. 78-89.
  13. Jaśkowski, W., Krawiec, K. and Wieloch, B. (2008a). Evolving strategy for a probabilistic game of imperfect information using genetic programming,(4): 281-294.
  14. Jaśkowski, W., Krawiec, K. and Wieloch, B. (2008b). Winning Ant Wars: Evolving a human-competitive game strategy using fitnessless selection,, pp. 13-24.
  15. Johnson, G. (1997). To test a powerful computer, play an ancient game,, July 29.
  16. Kim, K.-J., Choi, H. and Cho, S.-B. (2007). Hybrid of evolution and reinforcement learning for Othello players,, pp. 203-209.
  17. Krawiec, K. and Szubert, M. (2010). Coevolutionary temporal difference learning for small-board Go,, pp. 1-8.
  18. Lasker, E. (1960)., Dover Publications, New York, NY.
  19. Lubberts, A. and Miikkulainen, R. (2001). Co-evolving a Goplaying neural network,, pp. 14-19.
  20. Lucas, S. M. and Runarsson, T. P. (2006). Temporal difference learning versus co-evolution for acquiring Othello position evaluation,, pp. 52-59.
  21. Luke, S. (1998). Genetic programming produced competitive soccer softbot teams for RoboCup97,, pp. 214-222.
  22. Luke, S. (2010). ECJ 20—A Java-based Evolutionary Computation Research System
  23. Luke, S. and Wiegand, R. (2002). When coevolutionary algorithms exhibit evolutionary dynamics,, pp. 236-241.
  24. Mayer, H. A. (2007). Board representations for neural Go players learning by temporal difference,, pp. 183-188.
  25. Mechner, D. A. (1998). All systems Go,(1): 32-37.
  26. Michalewicz, Z. (1996).+=, Springer-Verlag, London.
  27. Miconi, T. (2009). Why coevolution doesn't "work": Superiority and progress in coevolution,, pp. 49-60.
  28. Monroy, G. A., Stanley, K. O. and Miikkulainen, R. (2006). Coevolution of neural networks using a layered Pareto archive,, pp. 329-336.
  29. Müller, M. (2009). Fuego at the Computer Olympiad in Pamplona 2009: A tournament report,, University of Alberta, Alberta.
  30. Pollack, J. B. and Blair, A. D. (1998). Co-evolution in the successful learning of backgammon strategy,(3): 225-240.
  31. Rosin, C. D. and Belew, R. K. (1997). New methods for competitive coevolution,(1): 1-29.
  32. Runarsson, T. P. and Lucas, S. (2005). Coevolution versus selfplay temporal difference learning for acquiring position evaluation in small-board Go,(6): 628-640.
  33. Samuel, A. L. (1959). Some studies in machine learning using the game of checkers,(3): 210-229.
  34. Schraudolph, N. N., Dayan, P. and Sejnowski, T. J. (2001). Learning to evaluate Go positions via temporal difference methods,N. Baba and L. C. Jain (Eds.), Studies in Fuzziness and Soft Computing, Vol. 62, Springer-Verlag, Berlin, Chapter 4, pp. 77-98.
  35. Silver, D., Sutton, R. and Müller, M. (2007). Reinforcement learning of local shape in the game of Go,, pp. 1053-1058.
  36. Singer, J. A. (2001). Co-evolving a neural-net evaluation function for Othello by combining genetic algorithms and reinforcement learning,, pp. 377-389.
  37. Stanley, K., Bryant, B. and Miikkulainen, R. (2005). Real-time neuroevolution in the NERO video game,(6): 653-668.
  38. Sutton, R. S. (1988). Learning to predict by the methods of temporal differences,(1): 9-44.
  39. Sutton, R. S. and Barto, A. G. (1998)., The MIT Press, Cambridge, MA.
  40. Szubert, M. (2010). cECJ—Coevolutionary Computation in Java
  41. Szubert, M., Jaśkowski, W. and Krawiec, K. (2009). Coevolutionary temporal difference learning for Othello,, pp. 104-111.
  42. Tesauro, G. (1995). Temporal difference learning and TD-Gammon,(3): 58-68.
  43. Watson, R. A. and Pollack, J. B. (2001). Coevolutionary dynamics in a minimal substrate,, pp. 702-709.
DOI: https://doi.org/10.2478/v10006-011-0057-3 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 717 - 731
Published on: Dec 21, 2011
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2011 Krzysztof Krawiec, Wojciech Jaśkowski, Marcin Szubert, published by University of Zielona Góra
This work is licensed under the Creative Commons License.