DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

Abstract

Abstract. We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the more favorable one. The training relies entirely on datasets of several million chess games, and no further domain specific knowledge is incorporated. The experiments show that the resulting neural network (referred to as DeepChess) is on a par with state-of-the-art chess playing programs, which have been developed through many years of manual feature selection and tuning. DeepChess is the first end-to-end machine learning-based method that results in a grandmaster-level chess playing performance.

Deploying tactical communication node vehicles with AlphaZero algorithm

“…The map of the battle area is similar to the board of chess and shogi in shape, and it also does not have the equivalence of mirror image and reversal of Go games [12]. Therefore, the number of data samples could not be expanded by the way of reversal of board as Go's.…”

Automated Chess Commentator Powered by Neural Chess Engine

“…And we design reasonable models and sufficient experiments to support our proposal. Chess engine has been researched for decades (Levy and Newborn, 1982;Baxter et al, 2000;David et al, 2017;Silver et al, 2017a). Powerful chess engines have already achieved much better game strength than human-beings (Campbell et al, 2002;Silver et al, 2017a).…”

“…And it can also make predictions in a continuous semantic space, increasing the capability and robustness for generation. Following advanced researches in neural chess engines (David et al, 2017;Silver et al, 2017a), we split the input raw board into 20 feature planes F for the sake of machine understanding. There are 12 planes for pieces' (pawn, rook, knight, bishop, queen, king) positions of each player, 4 planes for white's repetitions, black's repetitions, total moves, and moves with no progress, and 4 planes for 2 castling choices of each player.…”

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“…Nowadays, artificial intelligence (AI) has successfully been used for understanding human speech [1,2], competing at a high level in strategic game systems (such as Chess [3] and Go [4,5]), self-driving vehicles [6,7], and interpreting complex data [8,9]. Reinforcement learning (RL) [10,11], which is a vital branch of AI, has potential in the area of intelligent transportation.…”