The Roles of recognition processes and look-ahead search in time-constrained expert problem solving: Evidence from grandmaster level chess.

Chess has long served as an important standard task environment for research on human memory and problem-solving abilities and processes. In this paper, we report evidence on the relative importance of recognition processes and planning (look-ahead) processes in very high level expert performance in chess. The data show that the rated skill of a top-level grandmaster is only slightly lower when he is playing simultaneously against a half dozen grandmaster opponents than under tournament conditions that allow much more time for each move. As simultaneous play allows little time for look-ahead processes, the data indicate that recognition, based on superior chess knowledge, plays a much larger part in high-level skill in this task than does planning by looking ahead

On the limits of engine analysis for cheating detection in chess

The integrity of online games has important economic consequences for both the gaming industry and players of all levels, from professionals to amateurs. Where there is a high likelihood of cheating, there is a loss of trust and players will be reluctant to participate — particularly if this is likely to cost them money. Chess is a game that has been established online for around 25 years and is played over the Internet commercially. In that environment, where players are not physically present “over the board” (OTB), chess is one of the most easily exploitable games by those who wish to cheat, because of the widespread availability of very strong chess-playing programs. Allegations of cheating even in OTB games have increased significantly in recent years, and even led to recent changes in the laws of the game that potentially impinge upon players’ privacy. In this work, we examine some of the difficulties inherent in identifying the covert use of chess-playing programs purely from an analysis of the moves of a game. Our approach is to deeply examine a large collection of games where there is confidence that cheating has not taken place, and analyse those that could be easily misclassified. We conclude that there is a serious risk of finding numerous “false positives” and that, in general, it is unsafe to use just the moves of a single game as prima facie evidence of cheating. We also demonstrate that it is impossible to compute definitive values of the figures currently employed to measure similarity to a chess-engine for a particular game, as values inevitably vary at different depths and, even under identical conditions, when multi-threading evaluation is used

Strategic Behavior across Gender: A Comparison of Female and Male Expert Chess Players

This paper aims to measure differences in risk behavior among expert chess players. The study employs a panel data set on international chess with 1.4 million games recorded over a period of 11 years. The structure of the data set allows us to use individual fixed-effect estimations to control for aspects such as innate ability as well as other characteristics of the players. Most notably, the data contains an objective measure of individual playing strength, the so-called Elo rating. In line with previous research, we find that women are more risk-averse than men. A novel finding is that males choose more aggressive strategies when playing against female opponents even though such strategies reduce their winning probability.culture, gender, competitiveness, risk aversion, mixed-sex competition

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work