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
The advent of machine learning models that surpass human decision-making ability in complex domains ...
Bayesian probabilities are an efficient tool for addressing machine learning issues. However, becaus...
International audienceWe describe a generative Bayesian model of tactical attacks in strategy games,...
Evaluating agents in decision-making applications requires assessing their skill and predicting thei...
This paper proposes and demonstrates an approach, Skilloscopy, to the assessment of decision makers...
We extend the Bayesian skill rating system TrueSkill to infer entire time series of skills of player...
This paper develops and tests formulas for representing playing strength at chess by the quality of ...
The purpose of this paper is to provide a detailed technical protocol analysis of chess masters' eva...
Abstract. We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match o...
The bandit problem is a dynamic decision-making task that is simply described, well-suited to contro...
In this paper we introduce a chess program able to adapt its game strategy to its opponent, as well ...
Results from psychology show a connection between a speaker’s expertise in a task and the lan-guage ...
Abstract—Inferences about structured patterns in human de-cision making have been drawn from medium-...
Abstract. The assessment of chess players is both an increasingly attractive op-portunity and an unf...
For fifty years, computer chess has pursued an original goal of Artificial Intelligence, to produce ...
The advent of machine learning models that surpass human decision-making ability in complex domains ...
Bayesian probabilities are an efficient tool for addressing machine learning issues. However, becaus...
International audienceWe describe a generative Bayesian model of tactical attacks in strategy games,...
Evaluating agents in decision-making applications requires assessing their skill and predicting thei...
This paper proposes and demonstrates an approach, Skilloscopy, to the assessment of decision makers...
We extend the Bayesian skill rating system TrueSkill to infer entire time series of skills of player...
This paper develops and tests formulas for representing playing strength at chess by the quality of ...
The purpose of this paper is to provide a detailed technical protocol analysis of chess masters' eva...
Abstract. We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match o...
The bandit problem is a dynamic decision-making task that is simply described, well-suited to contro...
In this paper we introduce a chess program able to adapt its game strategy to its opponent, as well ...
Results from psychology show a connection between a speaker’s expertise in a task and the lan-guage ...
Abstract—Inferences about structured patterns in human de-cision making have been drawn from medium-...
Abstract. The assessment of chess players is both an increasingly attractive op-portunity and an unf...
For fifty years, computer chess has pursued an original goal of Artificial Intelligence, to produce ...
The advent of machine learning models that surpass human decision-making ability in complex domains ...
Bayesian probabilities are an efficient tool for addressing machine learning issues. However, becaus...
International audienceWe describe a generative Bayesian model of tactical attacks in strategy games,...