Can Deep Blue™ make us happy? Reflections on human and artificial expertise

Sadly, progress in AI has confirmed earlier conclusions, reached using formal domains, about the strict limits of human information processing and has also shown that these limits are only partly remedied by intuition. More positively, AI offers mankind a unique avenue to circumvent its cognitive limits: (1) by acting as a prosthesis extending processing capacity and size of the knowledge base; (2) by offering tools for studying our own cognition; and (3) as a consequence of the previous item, by developing tools that increase the quality and quantity of our own thinking. These ideas are illustrated with chess expertise

Chess Endgames and Neural Networks

The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques

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

Performance and prediction: Bayesian modelling of fallible choice in chess

Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks

Newspaper of the university of alaska southeast juneau campus

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