Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

Coulom, Rémi

doi:10.1007/978-3-540-87608-3_11

Cited by 68 publications

(54 citation statements)

References 9 publications

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“…Finally, there are approaches that include a time varying aspect on the quality of the players, i.e., they assume players improve over time [12], [2]. Since the experiments done in this paper involve static players, these approaches are again outside the scope of this paper.…”

Section: A Ranking Schemesmentioning

confidence: 99%

Predicting Dominance Rankings for Score-Based Games

Samothrakis

Pérez

Lucas

et al. 2016

IEEE Trans. Comput. Intell. AI Games

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Abstract-Game competitions may involve different player roles and be score-based rather than win/loss based. This raises the issue of how best to draw opponents for matches in ongoing competitions, and how best to rank the players in each role. An example is the Ms Pac-Man vs Ghosts Competition which requires competitors to develop software controllers to take charge of the game's protagonists: participants may develop software controllers for either or both Ms Pac-Man and the team of four ghosts. In this paper we compare two ranking schemes for win-loss games, Bayes Elo and Glicko. We convert the game into one of win/loss ("dominance") by matching controllers of identical type against the same opponent in a series of pair-wise comparisons. This implicitly creates a "solution concept" as to what a constitutes a good player. We analyse how many games are needed under two popular ranking algorithms, Glicko and Bayes Elo, before one can infer the strength of the players, according to our proposed solution concept, without performing an exhaustive evaluation. We show that Glicko should be the method of choice for online score-based game competitions.

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Section: A Ranking Schemesmentioning

confidence: 99%

Predicting Dominance Rankings for Score-Based Games

Samothrakis

Pérez

Lucas

et al. 2016

IEEE Trans. Comput. Intell. AI Games

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“…Each Swiss-style tournament 8 consisted of at least 200 games for each version of RLGO. After all matches were complete, the results were analysed by the bayeselo program (Coulom 2008) to establish an Elo rating for every program. Two benchmark programs were included in each tournament.…”

Section: Temporal-difference Search In Computer Gomentioning

confidence: 99%

Temporal-difference search in computer Go

2012

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Temporal-difference learning is one of the most successful and broadly applied solutions to the reinforcement learning problem; it has been used to achieve master-level play in chess, checkers and backgammon. The key idea is to update a value function from episodes of real experience, by bootstrapping from future value estimates, and using value function approximation to generalise between related states. Monte-Carlo tree search is a recent algorithm for high-performance search, which has been used to achieve masterlevel play in Go. The key idea is to use the mean outcome of simulated episodes of experience to evaluate each state in a search tree. We introduce a new approach to highperformance search in Markov decision processes and two-player games. Our method, temporal-difference search, combines temporal-difference learning with simulation-based search. Like Monte-Carlo tree search, the value function is updated from simulated experience; but like temporal-difference learning, it uses value function approximation and bootstrapping to efficiently generalise between related states. We apply temporal-difference search to the game of 9 × 9 Go, using a million binary features matching simple patterns of stones. Without any explicit search tree, our approach outperformed an unenhanced MonteCarlo tree search with the same number of simulations. When combined with a simple alpha-beta search, our program also outperformed all traditional (pre-Monte-Carlo) search and machine learning programs on the 9 × 9 Computer Go Server.

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“…They are determined from the results of games and not by the innate quality of the moves played: they therefore measure performance rather than underlying skill. There have been criticisms of the Elo approach [11] and improvements [10], [12], [13], [14] have been proposed. However, they are still results-based and affected over time by the changing player population.…”

Section: A Skill Rating In Chessmentioning

confidence: 99%

“…The mean value of the parameter c measures the innate quality of the moves and can be used as a player's skill rating. Moreover, the variance of the probability distribution, as in [12], [14], provides a measure of the uncertainty of the rating.…”

Section: P Rob[e(c)|ementioning

confidence: 99%

Skill rating by Bayesian inference

Fatta

Haworth

Regan

2009

2009 IEEE Symposium on Computational Intelligence and Data Mining

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Abstract-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.

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Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

Cited by 68 publications

References 9 publications

Predicting Dominance Rankings for Score-Based Games

Predicting Dominance Rankings for Score-Based Games

Temporal-difference search in computer Go

Skill rating by Bayesian inference

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