Skill Rating by Bayesian Inference
Di Fatta, G., Haworth, G. M. and Regan, K. W. (2009) Skill Rating by Bayesian Inference. In: Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on. Institute of Electrical and Electronics Engineers , Los Alamitos, CA 90720-1264 USA, pp. 89-94. ISBN 9781424427659
To link to this article DOI: 10.1109/CIDM.2009.4938634
Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess. Bayesian methods are used to infer the distribution of players’ skill levels from the moves they play rather than from their competitive results. The approach is used on large sets of games by players across a broad FIDE Elo range, and is in principle applicable to any scenario where high-value decisions are being made under pressure.
 A. De Groot, Het denken van den schaker. Amsterdam, Noord Hollandsche, 1946.  ——, Thought and choice in chess (2nd ed.) (Revised translation of De Groot, 1946). The Hague: Mouton Publishers, 1978.  F. Gobet, “Chess players’ thinking revisited,” Swiss Journal of Psychology, vol. 57, pp. 18–32, 1998.  F. Gobet and N. Charness, Expertise in chess, Chess and games. Cambridge handbook on expertise and expert performance. Cambridge, MA: Cambridge University Press, 2006.  A. Elo, The Rating of Chessplayers, Past and Present. Arco, ISBN 0-668-04721-6, 1978.  G.McC. Haworth, “Reference fallible endgame play,” ICGA Journal, vol. 26-2, pp. 81–91, 2002.  ——, “Gentlemen, stop your engines!” ICGA Journal, vol. 30-3, pp. 150–156, 2007.  R. A. Bradley and M. E. Terry, “Rank analysis of incomplete block designs. I. The method of paired comparisons,” Biometrika, vol. 39, pp. 324–345, 1952.  The SSDF rating list. [Online]. Available: http://ssdf.bosjo.net/list.htm  M. E. Glickman, “Parameter estimation in large dynamic paired comparison experiments,” Applied Statistics, vol. 48, pp. 377–394, 1999.  J. Beasley, The Mathematics of Games. Dover ISBN 0-4864-4976-9, 2006.  R. Herbrich, T. Minka, and T. Graepel, “TrueSkillTM: A Bayesian skill rating system,” in Advances in Neural Information Processing Systems (NIPS 2006). MIT Press, 2007, pp. 569–576.  P. Dangauthier, R. Herbrich, T. Minka, and T. Graepel, “TrueSkill Through Time: Revisiting the History of Chess,” in Advances in Neural Information Processing Systems (NIPS 2007). MIT Press, 2008, pp. 931–938.  R. Coulom, “Whole-History Rating: A Bayesian rating system for players of time-varying strength,” in Proceedings of the Conference on Computers and Games, Beijing, China, 2008.  M. Guid and I. Bratko, “Computer analysis of world chess champions,” ICGA Journal, vol. 29-2, pp. 65–73, 2006.  C. Sullivan. Comparison of great players, 2008. [Online]. Available: http://www.truechess.com/web/champs.html  P. Jansen, “Problematic positions and speculative play,” Computers, Chess and Cognition (Eds. T.A. Marsland and J. Schaeffer), Springer- Verlag, New York., pp. 9–32, 1990.  ——, Using Knowledge about the Opponent in Game-Tree Search. Ph.D. thesis, Carnegie-Mellon University, Pittsburgh, 1992.  ——, “KQKR: Awareness of a fallible opponent,” ICCA Journal, vol. 15-3, pp. 111–131, 1992.  ——, “KQKR: Assessing the utility of heuristics,” ICCA Journal, vol. 15-4, pp. 179–191, 1992.  ——, “KQKR: Speculatively thwarting a human opponent,” ICCA Journal, vol. 16-1, pp. 3–17, 1993.  S. Meyer-Kahlen. Definition of the Universal Chess Interface. [Online]. Available: http://tinyurl.com/65hxat  ChessBase GMBH, Mexikoring 35, D22297 Hamburg, Germany. Chessbase player database. [Online]. Available: http://www.chessbase.com  T. Gaksch. Toga II 1.3.1 Chess Engine. [Online]. Available: http://www.superchessengine.com/togaii.htm