Файл:Predicting Moves in Chess using Convolutional Neural Networks ConvChess.pdf
Barak Oshri Stanford University email@example.com Nishith Khandwala Stanford University firstname.lastname@example.org
We used a three layer Convolutional Neural Network (CNN) to make move predictions in chess. The task was defined as a two-part classification problem: a piece-selector CNN is trained to score which white pieces should be made to move, and move-selector CNNs for each piece produce scores for where it should be moved. This approach reduced the intractable class space in chess by a square root.
The networks were trained using 20,000 games consisting of 245,000 moves made by players with an ELO rating higher than 2000 from the Free Internet Chess Server. The piece-selector network was trained on all of these moves, and the move-selector networks trained on all moves made by the respective piece. Black moves were trained on by using a data augmentation to frame it as a move made by the white side.
The networks were validated against a dataset 20% the size of the training data. Our best model for the piece selector network produced a validation accuracy of 38.3%, and the move-selector networks for the pawn, rook, knight, bishop, queen, and king performed at 52.20%, 29.25%, 56.15%, 40.54%, 26.52% and 47.29%. The success of the convolutions in our model are reflected in how pieces that move locally perform better than those that move globally. The network was played as an AI against the Sunfish Chess Engine, drawing with 26 games out of 100 and losing the rest.
We recommend that convolution layers in chess deep learning approaches are useful in pattern recognition of small, local tactics and that this approach should be trained on and composed with evaluation functions for smarter overall play.
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