Computer analysis and comparison of chess players' game-playing styles

Today's computer chess programs are very good at evaluating chess positions. Research has shown that we can rank chess players by the quality of their game play, using a computer chess program. In the master's thesis Computer analysis and comparison of chess players' game-playing styles, we focus on the content analysis of chess games using a computer chess program's evaluation and attributes we determined for each individual position. We defined meaningful attributes that can be used for computer analysis and are also comprehensible to a chess player. Using the attributes, we built profiles with which we defined chess players' styles. We evaluated the quality of the profiles by automatically identifying a chess player in a set of chess players. The profile of the chess player in the set and the one outside of it was built using different chess games. Using the result of the analysis we refined the profiles structure. In doing so we were aided by the information gain of each attribute. The most suitable profile was used for searching for world chess champions with the most similar style to a chosen chess player. We also sorted the world chess champions into groups according to their style of play. Because these players are well known, we compared our groups with the chess players' actual styles and determined how successful we were. By using the developed profiles, we can help partly automate and ease a chess player's analysis of chess games. We believe that the methods used in building the profiles and for the subsequent analysis could be applied to other domains

Computer analysis and comparison of chess players' game-playing styles

Today's computer chess programs are very good at evaluating chess positions. Research has shown that we can rank chess players by the quality of their game play, using a computer chess program. In the master's thesis Computer analysis and comparison of chess players' game-playing styles, we focus on the content analysis of chess games using a computer chess program's evaluation and attributes we determined for each individual position. We defined meaningful attributes that can be used for computer analysis and are also comprehensible to a chess player. Using the attributes, we built profiles with which we defined chess players' styles. We evaluated the quality of the profiles by automatically identifying a chess player in a set of chess players. The profile of the chess player in the set and the one outside of it was built using different chess games. Using the result of the analysis we refined the profiles structure. In doing so we were aided by the information gain of each attribute. The most suitable profile was used for searching for world chess champions with the most similar style to a chosen chess player. We also sorted the world chess champions into groups according to their style of play. Because these players are well known, we compared our groups with the chess players' actual styles and determined how successful we were. By using the developed profiles, we can help partly automate and ease a chess player's analysis of chess games. We believe that the methods used in building the profiles and for the subsequent analysis could be applied to other domains

Gentlemen, stop your engines!

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 computer engines 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

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

Templates in chess memory: A mechanism for recalling several boards

This paper addresses empirically and theoretically a question derived from the chunking theory of memory (Chase & Simon, 1973): To what extent is skilled chess memory limited by the size of short-term memory (about 7 chunks)? This question is addressed first with an experiment where subjects, ranking from class A players to grandmasters, are asked to recall up to 5 positions presented during 5 seconds each. Results show a decline of percentage of recall with additional boards, but also show that expert players recall more pieces than is predicted by the chunking theory in its original form. A second experiment shows that longer latencies between the presentation of boards facilitate recall. In a third experiment, a Chessmaster gradually increases the number of boards he can reproduce with higher than 70% average accuracy to nine, replacing as many as 160 pieces correctly. To account for the results of these experiments, a revision of the Chase-Simon theory is proposed. It is suggested that chess players, like experts in other recall tasks, use long-term memory retrieval structures (Chase & Ericsson, 1982) or templates in addition to chunks in STM, to store information rapidly

Comparing Typical Opening Move Choices Made by Humans and Chess Engines

The opening book is an important component of a chess engine, and thus computer chess programmers have been developing automated methods to improve the quality of their books. For chess, which has a very rich opening theory, large databases of high-quality games can be used as the basis of an opening book, from which statistics relating to move choices from given positions can be collected. In order to find out whether the opening books used by modern chess engines in machine versus machine competitions are ``comparable'' to those used by chess players in human versus human competitions, we carried out analysis on 26 test positions using statistics from two opening books one compiled from humans' games and the other from machines' games. Our analysis using several nonparametric measures, shows that, overall, there is a strong association between humans' and machines' choices of opening moves when using a book to guide their choices.Comment: 12 pages, 1 figure, 6 table

Skill Rating by Bayesian Inference

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

On the Impact of Information Technologies on Society: an Historical Perspective through the Game of Chess

The game of chess as always been viewed as an iconic representation of intellectual prowess. Since the very beginning of computer science, the challenge of being able to program a computer capable of playing chess and beating humans has been alive and used both as a mark to measure hardware/software progresses and as an ongoing programming challenge leading to numerous discoveries. In the early days of computer science it was a topic for specialists. But as computers were democratized, and the strength of chess engines began to increase, chess players started to appropriate to themselves these new tools. We show how these interactions between the world of chess and information technologies have been herald of broader social impacts of information technologies. The game of chess, and more broadly the world of chess (chess players, literature, computer softwares and websites dedicated to chess, etc.), turns out to be a surprisingly and particularly sharp indicator of the changes induced in our everyday life by the information technologies. Moreover, in the same way that chess is a modelization of war that captures the raw features of strategic thinking, chess world can be seen as small society making the study of the information technologies impact easier to analyze and to grasp

Distinguishing humans from computers in the game of go: a complex network approach

We compare complex networks built from the game of go and obtained from databases of human-played games with those obtained from computer-played games. Our investigations show that statistical features of the human-based networks and the computer-based networks differ, and that these differences can be statistically significant on a relatively small number of games using specific estimators. We show that the deterministic or stochastic nature of the computer algorithm playing the game can also be distinguished from these quantities. This can be seen as tool to implement a Turing-like test for go simulators.Comment: 7 pages, 6 figure