Design and Development of Human Intuition based AI Chess playing system

Abstract

newline Chess is considered as the Drosophila of Artificial Intelligence (AI) since the research newlinein AI began with the concept of game playing and the game of chess became favourite topic in newlinethe Artificial Intelligence research history. Most state of the art chess engines relied upon more newlinetraditional search techniques like Minimax and Alpha beta till the recent success of systems newlinewhich are based on machine learning concept. Most chess engines are designed in such a way newlinethat they do not play intuitively like humans, but calculate as much variations as possible and newlinebase their decision upon that. Recent developments in game playing showed how AlphaZero newlineused new approach based on deep reinforcement learning. Chess engines can be improved in newlineterms of intuitive feel for the position by training the model with top human games and best newlinemove can be derived using our proposed hybrid optimization technique. newlineThis research proposes the Hybrid Fly-based artificial neural network (Hybridfly-ANN) model newlinefor attaining the maximum possible legal moves in the chess game rectifying the drawbacks of newlineconventional position evaluation strategies. The legal possible moves corresponding to a single newlinemove of the opponent is evaluated through the proposed method, out of which the one best newlineoptimal move is evaluated using the conventional mini-max algorithm. The significance of the newlineresearch relies on the proposed Hybrid Fly optimization algorithm that involve in tuning the newlineweights of the ANN classifier optimally, leading to enhanced performance of the system. The newlineeffectiveness of the proposed method is analyzed in terms of the evaluation metrics, such as newlineaccuracy, sensitivity, and specificity. newlineKeywords: Artificial intelligence, chess game, artificial neural network, minimax, and newlineoptimization.