用神经网络分析棋子价值，考虑全局棋盘状态提升预测精度。
PAWN: Piece Value Analysis with Neural Networks
- 用CNN自编码器提取棋盘整体位置特征，作为棋子价值预测的上下文。
- 在1200万对数据上测试，误差比传统方法降低16%，预测精度达0.65兵。
- 适用于需要精准评估棋子贡献的棋类AI系统，如象棋引擎优化。
在国际象棋中，棋子的相对价值取决于其与棋盘上所有其他棋子的空间关系，这一问题仍未解决。本文提出使用基于CNN的自编码器生成棋盘的潜在位置表示，将其作为输入融入多层感知机（MLP）架构，以改进棋子价值预测。基于从超过1200万局特级大师对局中提取的数据集，采用Stockfish 17生成真实标签，所提方法显著优于不依赖上下文的纯MLP模型，验证集平均绝对误差降低16%，相对棋子价值预测误差约为0.65兵。结果表明，将完整问题状态编码为上下文，可为个体组件贡献度预测提供有效归纳偏置。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。