Deeper model endgame analysis

Andrist, R. B.; Haworth, Guy

doi:10.1016/j.tcs.2005.09.044

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…They tend to depart from 0.00 as a decisive result becomes more obvious with increased depth of search, and they vary from engine to engine on the same position [12,13]. Only in the endgame zone where endgame tables (EGTs) have been computed does an infallible benchmark exist [3][4][5][6].…”

Section: The Chess Engine As Benchmarking Playermentioning

confidence: 99%

“…An important advantage is that his simple formula can be used iteratively as each new observation arrives. Skilloscopy is the name given to the assessment of skill by Bayesian Inference [2][3][4][5][6][7][8][9]. It proceeds from initial inherited or presumed probabilities p i that AP 'is' BP(c i ): AP's initial presumed apparent competence ac is therefore  i p i .…”

Section: Figmentioning

confidence: 99%

“…Skilloscopy was first conceived [3][4][5] in the context of that part of chess for which endgame tables (EGTs) hold perfect information. These EGTs provided infallible benchmark players BP c so the above caveats about the fallibility of BP do not apply.…”

Section: Figmentioning

confidence: 99%

“…Specifically, here, it is believed that MM  EV  AD  ASD  SR/SK  SRA. 5 The FIDE/ACP committee certainly regards MM/EV/AD as 'screening methods' but looks to more informed methods for definitive assessments [1].…”

Section: Summary and View Forwardmentioning

confidence: 99%

“…Bayesian inference as used by Haworth et al [2][3][4][5][6][7][8][9] is the natural vehicle for this as it combines an expression of prior belief with the modification of that belief in the light of subsequent evidence. Players may also be profiled from a 'cold start' if 'know nothing' priors are adopted.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Review of Skill Assessment: Performance, Prediction and Profiling

Haworth

Biswas

Regan

2015

Lecture Notes in Computer Science

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Abstract. The assessment of chess players is both an increasingly attractive opportunity and an unfortunate necessity. The chess community needs to limit potential reputational damage by inhibiting cheating and unjustified accusations of cheating: there has been a recent rise in both. A number of counter-intuitive discoveries have been made by benchmarking the intrinsic merit of players' moves: these call for further investigation. Is Capablanca actually, objectively the most accurate World Champion? Has ELO rating inflation not taken place? Stimulated by FIDE/ACP, we revisit the fundamentals of the subject to advance a framework suitable for improved standards of computational experiment and more precise results. Other games and domains look to chess as demonstrator of good practice, including the rating of professionals making high-value decisions under pressure, personnel evaluation by Multichoice Assessment and the organization of crowd-sourcing in citizen science projects. The '3P' themes of performance, prediction and profiling pervade all these domains.

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Section: The Chess Engine As Benchmarking Playermentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Section: Summary and View Forwardmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparative Review of Skill Assessment: Performance, Prediction and Profiling

Haworth

Biswas

Regan

2015

Lecture Notes in Computer Science

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Intrinsic Chess Ratings

Regan

Haworth

2011

AAAI

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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.

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Gentlemen, Stop Your Engines!

Haworth¹

2007

ICG

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For fifty years, computer chess has pursued an original goal of Artificial Intelligence, to produce a chess-engine to compete at the highest level. The goal has arguably been achieved, but that success has made it harder to answer questions about the relative playing strengths of man and machine. The proposal here is to approach such questions in a counter-intuitive way, handicapping or stopping-down chess engines so that they play less well. The intrinsic lack of man-machine games may be side-stepped by analysing existing games to place computerengines as accurately as possible on the FIDE ELO scale of human play. Move-sequences may also be assessed for likelihood if computer-assisted cheating is suspected.

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Deeper model endgame analysis

Cited by 4 publications

References 8 publications

A Comparative Review of Skill Assessment: Performance, Prediction and Profiling

A Comparative Review of Skill Assessment: Performance, Prediction and Profiling

Intrinsic Chess Ratings

Gentlemen, Stop Your Engines!

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