Admissibility in opponent-model search

Information Sciences

Volumn 154, Issue 3-4 SPEC., 2003, Pages 119-140

  Donkers, H H L M ^a   Uiterwijk, J W H M ^a   Van den Herik, H J ^a  

^a MAASTRICHT UNIVERSITY   (Netherlands)

Author keywords

Chess endgames; Heuristic search; Opponent modelling

Indexed keywords

FUNCTIONS; GAME THEORY; HEURISTIC METHODS; MATHEMATICAL MODELS;

HEURISTIC SEARCH;

INFORMATION SCIENCE;

EID: 0038344716     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(03)00046-X     Document Type: Article

References (21)

1. Anonymous referee (2002). Comments on the contents. Referee report on a previous version of this article.
2. D. Carmel, S. Markovitch, Learning models of opponent's strategies in game playing, Proceedings AAAI Fall Symposion on Games: Planning and Learning, Raleigh, NC, 1993, pp. 140-147.
3. Carmel D., Markovitch S. Pruning algorithms for multi-model adversary search. Artificial Intelligence. 99:1998;255-325.
4. J. Christensen, R.E. Korf, A unified theory of heuristic evaluation functions and its application to learning, Proceedings of the AAAI-86, 1986, pp. 148-152.
5. Donkers H.H.L.M., Uiterwijk J.W.H.M., van den Herik H.J. Probabilistic opponent-model search. Information Sciences. 135(3-4):2001;123-149.
6. Fenner C.J. Computer chess, news about the North American computer chess championship. The British Chess Magazine. 99(5):1979;193-200.
7. Fürnkranz J. Machine learning in computer chess: the next generation. ICCA Journal. 19(3):1996;147-161.
8. R. Hyatt, CRAFTY FTP site, ftp://ftp.cis.uab.edu/hyatt, 2002a.
9. R. Hyatt, Personal communication, 2002b.
10. Iida H., Uiterwijk J.W.H.M., van den Herik H.J., Herschberg I.S. Potential applications of opponent-model search. Part 1, the domain of applicability. ICCA Journal. 16(4):1993;201-208 Potential applications of opponent-model search. Part 1, the domain of applicability. Part 2, risks and strategies. ICCA Journal. 17(1):1993;10-14.
11. Iida H., Uiterwijk J.W.H.M., van den Herik H.J., Herschberg I.S. Potential applications of opponent-model search. Part 1, the domain of applicability. ICCA Journal. 16(4):1993;201-208 Potential applications of opponent-model search. Part 1, the domain of applicability. Part 2, risks and strategies. ICCA Journal. 17(1):1993;10-14.
12. Iida H., Kotani I., Uiterwijk J.W.H.M., van den Herik H.J. Gains and risks of OM search. van den Herik H.J., Uiterwijk J.W.H.M. Advances in Computer Chess 8. 1997;153-165 Universiteit Maastricht, Maastricht, The Netherlands.
13. Jansen P.J. KQKR: awareness of a fallible opponent. ICCA Journal. 15(3):1992;111-131.
14. Jansen P.J. KQKR: speculatively thwarting a human opponent. ICCA Journal. 16(1):1993;3-17.
15. Kocsis L., Uiterwijk J.W.H.M., van den Herik H.J. Learning time allocation using neural networks. Marsland T., Frank I. Computers and Games, Proceedings of the Second International Conference, CG 2000. Lecture Notes in Computer Science 2063. 2001;170-185 Springer Verlag, Berlin, Germany.
16. Levy D.N.L., Newborn M.M. How Computers Play Chess. 1991;Computer Science Press, W.H. Freeman and Co, New York, NY.
17. Nau D.S. Pathology on game trees revisited, and an alternative to minimaxing. Artificial Intelligence. 21(1-2):1983;221-244.
18. Pearl J. Heuristics, Intelligent Search Strategies for Computer Problem Solving. 1984;Addison-Wesley Publishing Company, Reading, MA.
19. Plaat A., Schaeffer J., Pijls W., de Bruin A. Best-first fixed-depth minimax algorithms. Artificial Intelligence. 87(1-2):1996;255-293.
20. Sackson S. A Gamut of Games. 1992;Dover, New York, NY.
21. Schneider F. Deep junior wins the 18th world microcomputer chess championship. ICGA Journal. 24(3):2001;173-179.