Evaluation of game tree search methods by game records

IEEE Transactions on Computational Intelligence and AI in Games

Volumn 2, Issue 4, 2010, Pages 288-302

  Takeuchi, Shogo ^a   Kaneko, Tomoyuki ^a   Yamaguchi, Kazunori ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Evaluation function; game tree search; games; Monte Carlo tree search

Indexed keywords

ACCURACY MEASURES; BASIC IDEA; COMPUTER GO; EVALUATION FUNCTION; GAME RECORDS; GAME TREE SEARCH; GAMES; HEURISTIC EVALUATION FUNCTIONS; MIN-MAX; MONOTONICITY; MONTE CARLO; TREE SEARCH;

HEURISTIC METHODS; HEURISTIC PROGRAMMING; PROBABILITY; TREES (MATHEMATICS);

MONTE CARLO METHODS;

EID: 78951470159     PISSN: 1943068X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCIAIG.2010.2102022     Document Type: Article

References (30)

1. L. Kocsis and C. Szepesvari, "Bandit based Monte-Carlo planning," in Machine Learning: ECML 2006. Berlin, Germany: Springer-Verlag, 2006, vol. 4212, pp. 282-293.
2. M. Müller, "Computer Go," Artif. Intell., vol. 134, no. 1-2, pp. 145-179, Jan. 2002.
3. S. Gelly and D. Silver, "Achieving master level play in 9×9 Computer Go," in Proc. 23rd AAAI Conf. Artif. Intell., 2008, pp. 1537-1540.
4. R. Coulom, "Efficient selectivity and backup operators in Monte-Carlo tree search," in Proc. Comput. Games, 2006, pp. 72-83.
5. M. Buro, "Improving heuristic mini-max search by supervised learning," Artif. Intell., vol. 134, no. 1-2, pp. 85-99, Jan. 2002.
6. J. van Rijswijck, "Learning from perfection: A data mining approach to evaluation function learning in awari," in Computer and Games, ser. Lecture Notes in Computer Science, T. A. Marsland and I. Frank, Eds. Berlin, Germany: Springer-Verlag, Oct. 2001, pp. 115-132, no. 2063.
7. D. Gomboc, M. Buro, and T. A. Marsland, "Tuning evaluation functions by maximizing concordance," Theor. Comput. Sci., vol. 349, no. 2, pp. 202-229, 2005.
8. A. L. Samuel, "Some studies in machine learning using the game of checkers," IBM J. Res. Dev., vol. 3, no. 3, pp. 210-229, 1959.
9. M. Buro, "From simple features to sophisticated evaluation functions," in Proc. 1st Int. Conf. Comput. Games, Tsukuba, Japan, Nov. 1998, pp. 126-145.
10. D. Gomboc, T. A. Marsland, and M. Buro, "Evaluation function tuning via ordinal correlation," in Advances in Computer Games. Berlin, Germany: Springer-Verlag, 2003, pp. 1-18.
11. G. Tesauro, "Temporal difference learning and TD-Gammon," Commun. ACM vol. 38, no. 3, pp. 58-68, Mar. 1995 [Online]. Available: http://www.research.ibm.com/massdist/tdl.html
12. J. Baxter, A. Tridgell, and L. Weaver, "Learning to play chess using temporal differences," Mach. Learn., vol. 40, no. 3, pp. 243-263, 2000.
13. M. L. Ginsberg, "GIB: Steps toward an expert-level bridge-playing program," in Proc. 16th Int. Joint Conf. Artif. Intell., 1999, pp. 584-589.
14. B. Sheppard, "World-championship-caliber scrabble," Artif. Intell., vol. 134, no. 1-2, pp. 241-275, Jan. 2002.
15. D. Billings, A. Davidson, J. Schaeffer, and D. Szafron, "The challenge of poker," Artif. Intell., vol. 134, no. 1-2, pp. 201-240, 2002.
16. B. Abramson, "Expected-outcome: A general model of static evaluation.," IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 2, pp. 182-193, Feb. 1990.
17. B. Brügmann, "Monte Carlo Go," Physics Dept., Syracuse Univ., Syracuse, NY, Tech. Rep., 1993.
18. B. Bouzy and B. Helmstetter, "Monte Carlo Go developments," in Advances in Computer Games. Many Games, Many Challenges.. Norwell, MA: Kluwer, 2003, pp. 159-174.
19. H. Yoshimoto, K. Yoshizoe, T. Kaneko, A. Kishimoto, and K. Taura, "Monte Carlo Go has away to go," in Proc. 21st Nat. Conf. Artif. Intell., 2006, pp. 1070-1075.
20. R. Coulom, "Efficient selectivity and backup operators in Monte-Carlo tree search," in Computers and Games, ser. Lecture Notes in Computer Science, H. J. van den Herik, P. Ciancarini, and H. H. L. M. Donkers, Eds. Berlin, Germany: Springer-Verlag, 2006, vol. 4630, pp. 72-83.
21. S. Gelly, Y. Wang, R. Munos, and O. Teytaud, "Modification of UCT with patterns in Monte-Carlo Go," INRIA, Tech. Rep. RR-6062, 2006.
22. G. M. J.-B. Chaslot, M. H. M. Winands, I. Szita, and H. J. van den Herik, "Cross-entropy for Monte-Carlo tree search," J. Int. Comput. Games Assoc., vol. 31, no. 3, pp. 145-156, Sep. 2008.
23. R. Coulom, "Computing Elo ratings of move patterns in the game of Go," J. Int. Comput. Games Assoc., vol. 30, no. 4, pp. 198-208, Dec. 2007.
24. D. Silver, R. Sutton, and M. Müller, "Sample-based learning and search with permanent and transient memories," in Proc. 25th Annu. Int. Conf. Mach. Learn., A. McCallum and S. Roweis, Eds., 2008, pp. 968-975.
25. N. Araki, "Move prediction and strength in Monte-Carlo Go program," M.S. thesis, Grad. School, Univ. Tokyo, Tokyo, Japan, 2008.
26. S. Takeuchi, T. Kaneko, and K. Yamaguchi, "Evaluation of Monte Carlo tree search and the application to Go," in Proc. IEEE Symp. Comput. Intell. Games, 2008, pp. 191-198.
27. S. Takeuchi, T. Kaneko, K. Yamaguchi, and S. Kawai, "Visualization and adjustment of evaluation functions based on evaluation values and win probability," in Proc. 22nd Nat. Conf. Artif. Intell., 2007, pp. 858-863 [Online]. Available: http://www.graco.c.utokyo.ac.jp/
28. R. Caruana and A. Niculescu-Mizil, "An empirical comparison of supervised learning algorithms using different performance metrics," in Proc. 23rd Int. Conf. Mach. Learn., 2006, pp. 161-168.
29. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, Jun. 2006.
30. H. Kume, "Shogi-Club-24-Man-Kyoku-Shu," (in Japanese) Naitai Shuppan Co., 2002, ISBN: 4931538037.