Associating shallow and selective global tree search with Monte Carlo for 9×9 Go

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3846 LNCS, Issue , 2006, Pages 67-80

  Bouzy, Bruno ^a  

^a UNIVERSITÉ PARIS DESCARTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER PROGRAMMING; GAME THEORY; ITERATIVE METHODS; MONTE CARLO METHODS; TREES (MATHEMATICS);

LEAF NODES; MONTE-CARLO PROGRAMS; RANDOM GAMES; SELECTIVE GLOBAL TREE SEARCH;

INTERACTIVE COMPUTER SYSTEMS;

EID: 33745179836     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11674399_5     Document Type: Conference Paper

References (24)

1. B. Abramson. Expected-outcome : a general model of static evaluation. IEEE Transactions on PAMI, 12:182-193, 1990.
2. E. Baum and W. Smith. A bayesian approach to relevance in game-playing. Artificial Intelligence, 97:195-242, 1997.
3. H. Berliner. The B* tree search algorithm; a best-first proof procedure. Artificial Intelligence, 12:23-40, 1979.
4. D. Billings, A. Davidson, J. Schaeffer, and D. Szafron. The challenge of poker. Artificial Intelligence, 134:201-240, 2002.
5. B. Bouzy. Indigo home page. www.math-info.univ-paris5.fr/~bouzy/INDIGO. html, 2002.
6. B. Bouzy. Associating knowledge and Monte Carlo approaches within a go program. In 7th Joint Conference on Information Sciences, pages 505-508, Raleigh, 2003.
7. B. Bouzy. The move decision process of Indigo. International Computer Game Association Journal, 26(1):14-27, March 2003.
8. B. Bouzy and T. Cazenave. Computer Go: an AI oriented survey. Artificial Intelligence, 132:39-103, 2001.
9. B. Bouzy and B. Helmstetter. Monte-Carlo Go developments. In H. Jaap van den Herik, Hiroyuki Iida, and Ernst A. Heinz, editors, 10th Advances in Computer Games, pages 159-174, Graz, 2003. Kluwer Academic Publishers.
10. B. Brügmann. Monte Carlo Go. www.joy.ne.jp/welcome/igs/Go/computer/- mcgo.tex.Z, 1993.
11. D. Bump. GNUGo home page, www.gnu.org/software/gnugo/devel.html, 2003.
12. M. Buro. Probcut: an effective selective extension of the alpha-beta algorithm. ICCA Journal, 18(2):71-76, 1995.
13. K. Chen. A study of decision error in selective game tree search. Information Sciences, 135:177-186, 2001.
14. G. Fishman. Monte-Carlo: Concepts, Algorithms, and Applications. Springer, 1996.
15. A. Junghanns. Are there practical alternatives to alpha-beta? ICCA Journal, 21(1):14-32, March 1998.
16. P. Kaminski. Vegos home page, www.ideanest.com/vegos/, 2003.
17. S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing. Science, May 1983.
18. R. Korf and D. Chickering. Best-first search. Artificial Intelligence, 84:299-337, 1994.
19. A.J. Palay. Searching with probabilities. Morgan Kaufman, 1985.
20. R. Rivest. Game-tree searching by min-max approximation. Artificial Intelligence, 34(1):77-96, 1988.
21. A. Sadikov, I. Bratko, and I. Kononenko. Search versus knowledge: an empirical study of minimax on KRK. In H. Jaap van den Herik, Hiroyuki Iida, and Ernst A. Heinz, editors, 10th Advances in Computer Games, pages 33-44, Graz, 2003. Kluwer Academic Publishers.
22. B. Sheppard. World-championship-caliber scrabble. Artificial Intelligence, 134:241-275, 2002.
23. G. Tesauro and G. Galperin. On-line policy improvement using Monte Carlo search. In Advances in Neural Information Processing Systems, pages 1068-1074, Cambridge MA, 1996. MIT Press.
24. E. van der Werf, H.J. van den Herik, and J.W.H.M. Uiterwijk. Solving Go on small boards. International Computer Game Association Journal, 26(2):92-107, June 2003.