Maia-2: 国际象棋中人机对齐的统一模型
Maia-2: A Unified Model for Human-AI Alignment in Chess
AI总结：
针对国际象棋中以往人类决策模型跨技能水平连贯性不足的问题，提出带技能感知注意力机制的统一模型Maia-2，显著提升了不同水平下的人机对齐效果，助力AI教学与决策研究。
摘要：
在越来越多的领域中，人工智能（AI）系统既超越了人类能力，又能精准建模人类行为。这带来了一种可能性：通过更具亲和力的AI伙伴以及对人类决策更深刻的洞察，在这些领域开展算法辅助教学。然而，实现这一目标的关键在于连贯地建模不同技能水平的人类行为。国际象棋是开展此类人机对齐研究的理想模型系统——它作为AI研究的核心测试平台有着悠久历史，拥有AlphaZero这类成熟的超人类AI系统，还能通过国际象棋等级分系统精准衡量技能水平。\n以往的国际象棋人类决策建模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提升的全谱系方面缺乏连贯性，最终作为AI伙伴和教学工具的效用也受到限制。在本研究中，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，并直接刻画人类的提升过程。考虑到人类学习的复杂非线性特性，我们引入了一种技能感知注意力机制，将玩家实力与编码后的国际象棋局面动态整合，使我们的模型能够对不断变化的玩家技能保持敏感。我们的实验结果表明，这一统一框架显著提升了不同专业水平范围内AI与人类玩家的对齐效果，为深化人类决策洞察和开发AI辅助教学工具铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
