There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making. Critical to achieving this goal, however, is coherently modeling human behavior at various skill levels. Chess is an ideal model system for conducting research into this kind of human-AI alignment, with its rich history as a pivotal testbed for AI research, mature superhuman AI systems like AlphaZero, and precise measurements of skill via chess rating systems. Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools. In this work, we propose a unified modeling approach for human-AI alignment in chess that coherently captures human style across different skill levels and directly captures how people improve. Recognizing the complex, non-linear nature of human learning, we introduce a skill-aware attention mechanism to dynamically integrate players' strengths with encoded chess positions, enabling our model to be sensitive to evolving player skill. Our experimental results demonstrate that this unified framework significantly enhances the alignment between AI and human players across a diverse range of expertise levels, paving the way for deeper insights into human decision-making and AI-guided teaching tools.

翻译：人工智能系统在越来越多的领域中不仅超越了人类能力，还能精确建模人类行为。这为通过更具亲和力的AI伙伴以及更深入的人类决策洞察，在这些领域实现算法辅助教学提供了可能。然而，实现这一目标的关键在于对不同技能水平的人类行为进行连贯建模。国际象棋是研究此类人机对齐的理想模型系统，它拥有作为AI研究关键测试平台的悠久历史、成熟的超人类AI系统（如AlphaZero），以及通过国际象棋等级分系统实现的精准技能度量。以往对国际象棋人类决策建模的研究使用完全独立的模型来捕捉不同技能水平的人类风格，这意味着它们在适应人类技能提升的完整谱系上缺乏连贯性，最终作为AI伙伴和教学工具的有效性受限。在本研究中，我们提出了一种国际象棋人机对齐的统一建模方法，该方法能连贯地捕捉不同技能水平的人类风格，并直接刻画人类的进步过程。认识到人类学习过程的复杂非线性特性，我们引入了一种技能感知注意力机制，动态整合棋手优势与编码后的棋局信息，使模型能够敏感响应棋手技能的动态演变。实验结果表明，这一统一框架显著增强了AI与不同专业水平人类棋手之间的对齐度，为深入理解人类决策和开发AI引导的教学工具开辟了新路径。