Learning to play chess using temporal differences

Machine Learning

Volumn 40, Issue 3, 2000, Pages 243-263

  Baxter, Jonathan ^a   Tridgell, Andrew ^a   Weaver, Lex ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Backgammon; Chess; Neural network; Tdleaf; Temporal difference learning

Indexed keywords

CLIENT SERVER COMPUTER SYSTEMS; FUNCTION EVALUATION; GAME THEORY; INTERACTIVE COMPUTER SYSTEMS; INTERNET; NEURAL NETWORKS; ONLINE SYSTEMS; TREES (MATHEMATICS);

GAME TREE SEARCH; INTERNET CHESS SERVERS; TEMPORAL DIFFERENCE LEARNING;

LEARNING ALGORITHMS;

EID: 0034275416     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007634325138     Document Type: Article

References (16)

1. Beal, D. F. and Smith, M. C. (1997). Learning piece values using temporal differences. Journal of The International Computer Chess Association.
2. Bertsekas, D. P. and Tsitsiklis, J. N. (1996). Neuro-Dynamic Programming. Athena Scientific.
3. Marsland, T. A. and Schaeffer, J. (1990). Computers, Chess and Cognition. Springer Verlag.
4. Plaat, A., Schaeffer, J., Pijls, W., and de Bruin, A. (1996). Best-first fixed-depth minmax algorithms. Artificial Intelligence, 87, 255-293.
5. Pollack, J., Blair, A., and Land, M. (1996). Coevolution of a backgammon player. In Proceedings of the Fifth Artificial Life Conference, Nara, Japan.
6. Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3, 210-229.
7. Schaeffer, J. (1989). The history of heuristic and alpha-beta search enhancements in practice. IEEE Transactions on Pattern Analysis and Machine Learning, 11(11), 1203-1212.
8. Schraudolph, N., Dayan, P., and Sejnowski, T. (1994). Temporal difference learning of position evaluation in the game of go. In J. Cowan, G. Tesauro, and J. Alspector (Eds.), Advances in Neural Information Processing Systems 6. San Fransisco: Morgan Kaufmann.
9. Sutton, R. (1988). Learning to predict by the method of temporal differences. Machine Learning, 3, 9-44.
10. Sutton, R. S. and Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge MA: MIT Press. ISBN 0-262-19398-1.
11. Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning, 8, 257-278.
12. Tesauro, G. (1994). TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation, 6, 215-219.
13. Thrun, S. (1995). Learning to play the game of chess. In G. Tesauro, D. Touretzky, and T. Leen (Eds.), Advances in Neural Information Processing Systems 7. San Fransisco: Morgan Kaufmann.
14. Tridgell, A. (1997). KnightCap - A parallel chess program on the AP1000+. In Proceedings of the Seventh Fujitsu Parallel Computing Workshop, Canberra, Australia, ftp://samba.anu.edu.au/tridge/knightcap_pcw97.ps.gz source code: http://wwwsysneg.anu.edu.au/lsg.
15. Tsitsiklis, J. N. and Roy, B. V. (1997). An analysis of temporal difference learning with function approximation. IEEE Transactions on Automatic Control, 42(5), 674-690.
16. Walker, S., Lister, R., and Downs, T. (1993). On self-learning patterns in the othello board game by the method of temporal differences. In C. Rowles, H. Liu, and N. Foo (Eds.), Proceedings of the 6th Australian Joint Conference on Artificial Intelligence (pp. 328-333). Melbourne: World Scientific.