Neural Network Research Improves Chess Piece Value Prediction Using Full Board Context

A recent study reveals that utilizing the entire chess board state through latent position representations significantly boosts machine learning models in forecasting piece values. This research, available on arXiv with the identifier 2604.15585v1, indicates that employing a CNN-based autoencoder to capture the complete board context offers valuable inductive bias for assessing the contributions of individual pieces. The researchers compiled over 12 million piece-value pairs from Grandmaster-level matches, with ground-truth labels produced by Stockfish 17. Their improved piece value predictor surpasses context-independent MLP-based systems, achieving a 16% reduction in validation mean absolute error. The model's predictions are within roughly 0.65 pawns of the actual relative piece values, addressing the challenge of spatially dependent piece value predictions.