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In any legal chess position, we define an attacked-square entropy $S$ for either Black or White pieces in terms of the square occupation probability $p_i=m_i∕M$, where $m$ is the number of all possible movements to square $i$ (free or occupied by an opponent’s piece) and $M$ is the total mobility defined as the sum of all possible movements. Thus, each attacked square contributes to the entropy according to its received “firepower” concentration. A simpler nonsquare-dependant equiprobable entropy $S_e$ in terms of equal probabilities $p_e=1∕M$ always yields $S_e-S\ge 0$. On average, the difference $S_e-S$ is very large in the Opening phase and $S$ decreases faster for lower ranked players after move 25. A major cause of the reduction of $S$ during a game is material loss, which is an irreversible process. By game outcome, gaps in average $〈S〉$ among winners, draws and losers are larger for Amateur players than for Elite players, both in the Middlegame and Endgame. Statistically, Elite players exhibit narrower dispersions in $S$. Also, the entropy rates of the Elite level fluctuate much less than the entropies of other levels. Density of attacks in the four-square central zone is very high in the Opening, specially for Elite players.

Game playing is commonly cited in debates concerning human versus machine intelligence, and Chess is often at the center of such debates. However, the role of Chess in delineating the difference between natural and artificial intelligence has been significantly diminished since a World Chess Champion lost in a tournament against a computer. Computer brute force is regularly blamed for the human defeat. This paper proposes a Quantum Chess Board in an attempt to bring back some equilibrium, putting humans and computers on an ostensibly equal footing when faced with the uncertainties of quantum physics.

Our research demonstrates the utility of incorporating pattern recognition with machine learning in adversarial domains. We use induction to recognize cognitive behavioral patterns of an adversary and use those patterns for tactical planning in the adversarial domain. Results of our methodology for the application domain of chess are presented.

The art of mimicking a human’s responses and behavior in a programming machine is called Artificial intelligence (AI). AI has been incorporated in games in such a way to make them interesting, especially in chess games. This paper proposes a hybrid optimization tuned neural network (NN) to establish a winning strategy in the chess game by generating the possible next moves in the game. Initially, the images from Portable Game Notation (PGN) file are used to train the NN classifier. The proposed Locust Mayfly algorithm is utilized to optimally tune the weights of the NN classifier. The proposed Locust Mayfly algorithm inherits the characteristic features of hybrid survival and social interacting search agents. The NN classifier involves in finding all the possible moves in the board, among which the best move is obtained using the mini-max algorithm. At last, the performance of the proposed Locust mayfly-based NN method is evaluated with help of the performance metrics, such as specificity, accuracy, and sensitivity. The proposed Locust mayfly-based NN method attained a specificity of 98%, accuracy of 98%, and a sensitivity of 98%, which demonstrates the productiveness of the proposed mayfly-based NN method in pruning.

We examine a strong chess-endgame player, previously developed by us through genetic programming, focusing on the player's emergent capabilities and tactics in the context of a chess match. First, we provide a detailed description of the evolutionary approach by which our player was developed. Then, using a number of methods we analyze the evolved player's building blocks and their effect on play level. We conclude that evolution has found combinations of building blocks that are far from trivial and cannot be explained through simple combination — thereby indicating the possible emergence of complex strategies.

An Elo score is a known measure of past performance in chess and other games. This paper investigates the impact of Elo ratings on chess game results and whether this measure can be used to predict future performance in matches. To achieve the aim, various machine learning classification techniques have been evaluated using chess data sourced from an online chess server. We examine how much influence the Elo score has on the prediction power of these techniques based on classifiers they derive. The prime objective of this experiment is to accurately predict the winner of a chess game from attributes that are available before the game starts. We are particularly interested in how large an impact the Elo score has on the prediction when compared with other features. Empirical results reported that classifiers derived by artificial neural network (Multilayer Perceptron), Decision Tree (J48/C4.5), Rule Induction (JRip/RIPPER) and Probabilistic (Naïve Bayes) showed how useful the Elo is at predicting chess results, at least on the dataset considered, improving classifiers’ performance with respect to accuracy, precision, recall and area under curve, among others.

The past history of chess and its past variants is examined based on a new measure of game refinement and complexity.The history of chess variants can be divided into the three stages: the origin with moderate complexity and refinement, many diverging variants with high complexity and low refinement, and the modern sophisticated chess with low complexity and high refinement.