用棋步和用时直接预测棋手等级分，每步后更新评分。
Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM
- 结合棋局位置与用时数据，用CNN-LSTM逐步预测等级分。
- 测试集平均误差182分，优于传统增量更新方法。
- 适合需实时评估实力的场景，如反作弊或教学分析。
现有国际象棋等级分系统采用增量更新，难以准确反映快速进步或长期不练玩家的真实水平。为此，我们提出一种直接从对局走法与用时估算等级分的方法。基于Lichess平台构建包含超百万局的基准数据集，涵盖多种时间控制，包含完整走法序列与计时信息。模型采用卷积神经网络提取局面特征，并与用时数据融合输入双向长短期记忆网络，实现每步后的等级分预测。测试集上平均绝对误差达182分。此外，在2024年IEEE Big Data Cup国际象棋谜题难度预测竞赛中，该模型亦取得具有竞争力的表现。本方法是首个无需人工设计特征、且能输出每步后评级预测的模型，展示了基于走法的等级分估计在提升评级系统及反作弊等应用中的潜力。
原文摘要 · Abstract (English)
Current chess rating systems update ratings incrementally and may not always accurately reflect a player's true strength at all times, especially for rapidly improving players or very rusty players. To overcome this, we explore a method to estimate player ratings directly from game moves and clock times. We compiled a benchmark dataset from Lichess with over one million games, encompassing various time controls and including move sequences and clock times. Our model architecture comprises a CNN to learn positional features, which are then integrated with clock-time data into a Bidirectional LSTM, predicting player ratings after each move. The model achieved an MAE of 182 rating points on the test data. Additionally, we applied our model to the 2024 IEEE Big Data Cup Chess Puzzle Difficulty Competition dataset, predicted puzzle ratings and achieved competitive results. This model is the first to use no hand-crafted features to estimate chess ratings and also the first to output a rating prediction after each move. Our method highlights the potential of using move-based rating estimation for enhancing rating systems and potentially other applications such as cheating detection.
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。