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Intrinsic chess rating

Regan, K. W. and Haworth, G. M. (2011) Intrinsic chess rating. In: AAAI-11: the 25th AAAI Conference on Artificial Intelligence, 07-11 August 2011, San Francisco, USA, pp. 834-839. (ISBN: 9781577355076)

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Official URL: http://www.aaai.org/ocs/index.php/AAAI/AAAI11/pape...

Abstract/Summary

This paper develops and tests formulas for representing playing strength at chess by the quality of moves played, rather than by the results of games. Intrinsic quality is estimated via evaluations given by computer chess programs run to high depth, ideally so that their playing strength is sufficiently far ahead of the best human players as to be a `relatively omniscient' guide. Several formulas, each having intrinsic skill parameters s for `sensitivity' and c for `consistency', are argued theoretically and tested by regression on large sets of tournament games played by humans of varying strength as measured by the internationally standard Elo rating system. This establishes a correspondence between Elo rating and the parameters. A smooth correspondence is shown between statistical results and the century points on the Elo scale, and ratings are shown to have stayed quite constant over time. That is, there has been little or no `rating inflation'. The theory and empirical results are transferable to other rational-choice settings in which the alternatives have well-defined utilities, but in which complexity and bounded information constrain the perception of the utility values.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science
ID Code:19778
Uncontrolled Keywords:Computer games, chess, sequential decision making, probabilistic inference, machine learning, data mining, statistics

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