Temporal difference learning applied to a high-performance game-playing program

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2001, Pages 529-534

  Schaeffer, Jonathan ^a   Hlynka, Markian ^a   Jussila, Vili ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

EVALUATION FUNCTION; GAME-PLAYING PROGRAMS; MAN MACHINES; TD-LEARNING; TEMPORAL DIFFERENCE LEARNING; WORLD CLASS;

ARTIFICIAL INTELLIGENCE;

EID: 0038145011     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. T. Anantharaman. A Statistical Study of Selective Min-Max Search. PhD thesis, Carnegie Mellon University, 1991.
2. J. Baxter, A. Tridgell, and L. Weaver. Experiments in parameter learning using temporal differences. ICCA Journal, 21(2):84-99, 1998.
3. J. Baxter, A. Tridgell, and L. Weaver. KnightCap: A chess program that learns by combining TD(λ) with game-tree search. ICML, pages 28-36, 1998.
4. J. Baxter, A. Tridgell, and L. Weaver. Learning to play chess using temporal differences. Machine Learning, 40(3):243-263, 2000.
5. D. Beal. Learning piece values using temporal differences. ICCA Journal, 20(3):147-151, 1997.
6. H. Berliner, G. Goetsch, M. Campbell, and C. Ebeling. Measuring the performance potential of chess programs. Artificial Intelligence, 43(1):7-21, 1990.
7. A. Brungger, A. Marzetta, K. Fukuda, and J. Nievergelt. The parallel search bench ZRAM and its applications. Annals of Operations Research, 90:45-63, 1999.
8. M. Buro. Statistical feature combination for the evaluation of game positions. JAIR, 3:373-382, 1995.
9. M. Buro. Improving heuristic mini-max search by supervised learning. Artificial Intelligence, 2001. To appear.
10. T. Fawcett and P. Utgoff. Automatic feature generation for problem solving systems. ICML, pages 144-153, 1992.
11. J. Romein. Multigame - An Environment for Distributed Game-Tree Search. PhD thesis, Vrije Universiteit, 2000.
12. J. Schaeffer. One Jump Ahead: Challenging Human Supremacy in Checkers. Springer Verlag, 1997.
13. R. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
14. G. Tesauro. Temporal difference learning and TD-Gammon. CACM, 38(3):58-68, 1995.
15. M. van der Muelen. Weight assessment in evaluation functions. Advances in Computer Chess 5, pages 81-89, 1989.
16. J. van Rijswijck. Learning from perfection (a data mining approach to evaluation function learning in awari). 2nd International Conference on Computers and Games, 2001. To appear.