Coevolving partial strategies for the game of Go

Proceedings of the 2008 International Conference on Genetic and Evolutionary Methods, GEM 2008

Volumn , Issue , 2008, Pages 312-318

  Drake, Peter ^a   Chen, Yung Pin ^a  

^a LEWIS AND CLARK COLLEGE   (United States)

Author keywords

Coevolution; Game play; Genetic algorithms; Go; Tree search

Indexed keywords

ASIAN GAMES; COEVOLUTION; COEVOLUTION ALGORITHMS; DOMAIN-SPECIFIC KNOWLEDGE; GAME PLAY; GO; GRAND CHALLENGES; MULTI-THREADED; MULTIPLE SEARCHES; RANDOM SAMPLINGS; TREE SEARCH;

ARTIFICIAL INTELLIGENCE; ELECTRON MULTIPLIERS; GENETIC ALGORITHMS;

GAME THEORY;

EID: 62949178860     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Baker, K. The Way to Go. New York, NY: American Go Association, 1986.
2. Berlekamp, E. and Wolfe, D. Mathematical Go: Chilling Gets the Last Point. Wellesley, Massachusetts: A K Peters, 1994.
3. Bouzy, B., and Cazenave, T. Computer Go: an AI Oriented Survey. Artificial Intelligence, 132(1):39-103, 2001.
4. Cazenave, T. and Jouandeau, N. On the Parallelization of UCT. Computer Games Workshop, 93-101, 2007.
5. Coulom, R. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. Lecture Notes in Computer Science, Vol. 4630, 72-83, Springer, 2007.
6. Davies, J. Life and Death. Tokyo, Japan: The Ishi Press, 1975.
7. Drake, P. and Uurtamo, S. Move Ordering vs Heavy Playouts: Where Should Heuristics Be Applied in Monte Carlo Go? In Proceedings of the 3rd annual North American Game-On Conference, 2007.
8. Eiben, A.E. and Smith, J.E. Introduction to Evolutionary Computing. Springer Berlin/Heidelberg, 2003.
9. Gelly, S. and Silver, D. Combining Online and Offline Knowledge in UCT. International Conference of Machine Learning (Corvallis, Oregon), 273-280, 2007.
10. Gelly, S., Wang, Y, Munos, R., and Teytaud, O. Modification of UCT with Patterns in Monte-Carlo Go. Technical Report 6062, INRIA.
11. Holland, J. Adaptation in Natural and Artificial Systems. Cambridge, Massachusetts: The MIT Press, 1992.
12. Kim, J. and Jeong, S. Learn to Play Go, Vol. 1, 2nd ed. Sheboygan, WI: Good Move Press, 1997.
13. Kim, J. and Jeong, S. Learn to Play Go, Vol. 2, 2nd ed. Denver, CO: Good Move Press, 1998.
14. Kocsis, L. and Szepesvari, C. Bandit Based Monte-Carlo Planning. In 15th European Conference on Machine Learning, 282-293, 2006.
15. Konidaris, G., Shell, D., and Oren, N. Evolving Neural Networks for the Capture Game. In Proceedings of the SAICSIT Postgraduate Symposium (Port Elizabeth, South Africa), 2002.
16. Koza, J. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, Massachusetts: The MIT Press, 1992.
17. Koza, J. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, Massachusetts: The MIT Press, 1994.
18. Mayer, H. and Maier, P. Coevolution of Neural Go Players in a Cultural Environment. IEEE Congress on Evolutionary Computation, Vol. 2, 1017-1024, 2005.
19. Miconi, T. and Channon, A. The N-Strikes-Out Algorithm: A Steady-State Algorithm for Coevolution. IEEE Congress on Evolutionary Computation, 1639-1646, 2006.
20. Müller, M. Computer Go. Artificial Intelligence, 134 (1-2):145-179, 2002.
21. Pratola, M. and Wolf, T. Optimizing GoTools' Search Heuristics Using Genetic Algorithms. ICGA Journal, Vol. 26, 28-48, 2003.
22. Rosin, C. Coevolutionary Search Among Adversaries. Ph.D. thesis, University of California, San Diego, 1997.
23. Rosin, C. and Belew, R. Methods for Competitive Coevolution: Finding Opponents Worth Beating. Proceedings of the 6th International Conference on Genetic Algorithms, 373-381, 1995.
24. Rutquist, P. Evolving an Evaluation Function to Play Go. Technical report, Ecole Polytechnique, 2000.
25. Stanley, K and Miikkulainen, R. Evolving a Roving Eye for Go. Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY: Springer-Verlag, 2004.
26. Watson, R. and Pollack, J. Coevolutionary Dynamics in a Minimal Substrate. Proceedings of the Genetic and Evolutionary Computation Conference, 702-709, 2001.
27. Wu, A. and Lindsay, R. Empirical Studies of the Genetic Algorithms with Non-coding Segments. Evolutionary Computation, Vol. 3, Issue 2, 121-147, 1995.