Chess, a deterministic game with perfect information, has long served as a benchmark for studying strategic decision-making and artificial intelligence. Traditional chess engines or tools for analysis primarily focus on calculating optimal moves, often neglecting the variability inherent in human chess playing, particularly across different skill levels. To overcome this limitation, we propose a novel and computationally efficient move prediction framework that approaches chess move prediction as a behavioral analysis task. The framework employs n-gram language models to capture move patterns characteristic of specific player skill levels. By dividing players into seven distinct skill groups, from novice to expert, we trained separate models using data from the open-source chess platform Lichess. The framework dynamically selects the most suitable model for prediction tasks and generates player moves based on preceding sequences. Evaluation on real-world game data demonstrates that the model selector module within the framework can classify skill levels with an accuracy of up to 31.7\% when utilizing early game information (16 half-moves). The move prediction framework also shows substantial accuracy improvements, with our Selector Assisted Accuracy being up to 39.1\% more accurate than our benchmark accuracy. The computational efficiency of the framework further enhances its suitability for real-time chess analysis.

翻译：国际象棋作为一种具有完美信息的确定性博弈，长期以来一直是研究战略决策与人工智能的基准。传统的国际象棋引擎或分析工具主要侧重于计算最优走法，往往忽视了人类棋手固有的变异性，尤其是在不同技能水平之间的差异。为克服这一局限，我们提出了一种新颖且计算高效的走法预测框架，将国际象棋走法预测视为行为分析任务。该框架采用n元语言模型来捕捉特定玩家技能水平所特有的走法模式。通过将棋手划分为从新手到专家的七个不同技能组，我们利用开源国际象棋平台Lichess的数据训练了独立的模型。该框架动态选择最合适的模型进行预测任务，并基于先前的走法序列生成玩家走法。在真实棋局数据上的评估表明，框架中的模型选择器模块在使用早期对局信息（16个半回合）时，技能水平分类准确率最高可达31.7%。走法预测框架亦显示出显著的准确率提升，其中选择器辅助准确率较基准准确率最高可提升39.1%。该框架的计算效率进一步增强了其实时棋局分析的适用性。