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，而且能够精确地模拟人类行为。这为通过更具亲和力的人工智能伙伴以及对人类决策的深入洞察，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，因为它拥有作为人工智能研究关键测试平台的悠久历史、成熟的超人类人工智能系统（如AlphaZero），以及通过国际象棋等级分系统对技能进行的精确度量。先前关于国际象棋中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，这意味着这些模型在适应人类技能全面提升方面缺乏连贯性，最终限制了其作为人工智能伙伴和教学工具的有效性。在本工作中，我们提出了一种用于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，并直接捕捉人类如何进步。认识到人类学习复杂、非线性的本质，我们引入了一种技能感知注意力机制，以动态地将棋手的优势与编码后的棋局位置相结合，从而使我们的模型能够对棋手不断发展的技能保持敏感。我们的实验结果表明，这一统一框架显著增强了人工智能与不同专业水平人类棋手之间的对齐，为深入理解人类决策和开发人工智能引导的教学工具铺平了道路。