PAWN: Piece Value Analysis with Neural Networks

2026-04-16

AI Summary

This study addresses the challenge of accurately estimating the dynamic relative values of chess pieces, which vary significantly with the global board context. The authors propose a novel approach that first employs a convolutional neural network (CNN) autoencoder to extract latent representations of full-board positions, capturing rich contextual information. These representations are then fed into a multilayer perceptron (MLP) to regress piece values. By explicitly incorporating global board encoding into piece valuation—a first in this domain—the method introduces a powerful inductive bias for modeling individual piece contributions. Evaluated on a large-scale dataset of grandmaster games annotated by Stockfish 17, the model achieves a 16% reduction in mean absolute error on the validation set compared to a context-free MLP baseline, yielding a prediction error of approximately 0.65 pawns.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Predicting the relative value of any given chess piece in a position remains an open challenge, as a piece's contribution depends on its spatial relationships with every other piece on the board. We demonstrate that incorporating the state of the full chess board via latent position representations derived using a CNN-based autoencoder significantly improves accuracy for MLP-based piece value prediction architectures. Using a dataset of over 12 million piece-value pairs gathered from Grandmaster-level games, with ground-truth labels generated by Stockfish 17, our enhanced piece value predictor significantly outperforms context-independent MLP-based systems, reducing validation mean absolute error by 16% and predicting relative piece value within approximately 0.65 pawns. More generally, our findings suggest that encoding the full problem state as context provides useful inductive bias for predicting the contribution of any individual component.

Problem

• piece value prediction
• chess
• context-dependent valuation
• spatial relationships
• board state

Innovation

• neural networks
• piece value prediction
• chess board representation
• CNN autoencoder
• context-aware modeling