用SHAP分析棋子对引擎评分的贡献，让棋局评估更可解释。
Towards Piece-by-Piece Explanations for Chess Positions with SHAP
- 将棋子视为特征，通过逐个移除计算其对评分的贡献。
- 实现局部忠实且人类可理解的棋局解释，与经典教学法呼应。
- 适合棋手训练、引擎对比和可解释AI研究者使用。
当前国际象棋引擎虽能给出精确但不透明的评估（通常以分厘为单位），难以揭示各棋子或棋形的具体影响。本文探索将SHAP（SHapley Additive exPlanations）方法应用于国际象棋分析，旨在将引擎评分归因于棋盘上特定棋子。通过将棋子视为特征并系统性地移除它们，我们计算出每颗棋子对总评分的加性贡献，实现局部忠实且人类可读的解释。该方法借鉴经典棋类教学中“心理去棋”的思路，并结合现代可解释人工智能技术。我们的方法为可视化、人类训练及引擎比较提供了新可能。论文附带代码与数据，以促进可解释国际象棋智能的后续研究。
原文摘要 · Abstract (English)
Contemporary chess engines offer precise yet opaque evaluations, typically expressed as centipawn scores. While effective for decision-making, these outputs obscure the underlying contributions of individual pieces or patterns. In this paper, we explore adapting SHAP (SHapley Additive exPlanations) to the domain of chess analysis, aiming to attribute a chess engines evaluation to specific pieces on the board. By treating pieces as features and systematically ablating them, we compute additive, per-piece contributions that explain the engines output in a locally faithful and human-interpretable manner. This method draws inspiration from classical chess pedagogy, where players assess positions by mentally removing pieces, and grounds it in modern explainable AI techniques. Our approach opens new possibilities for visualization, human training, and engine comparison. We release accompanying code and data to foster future research in interpretable chess AI.
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