发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出对齐博弈框架，通过识别和修复人机协作中的概念错位，利用对齐动作干预情境与推理，支持任务充分的概念对齐。
AI 中文摘要
使用中的概念意义由情境、任务、目标和先验知识所塑造。例如，请求制作一张“对五岁孩子具有视觉吸引力”的海报，可能会让一位协作者联想到鲜艳的色彩和卡通图像，而另一位协作者则可能联想到更少的文字、粗体形状和视觉简洁性。我们将这种与任务相关的差异称为概念错位。我们引入对齐博弈，这是一个在人机交互过程中使这些差异可见且可修复的框架。借鉴情境化概念化理论，我们根据相关属性、值、关系、约束和优先级来刻画任务特定的概念框架。然后，我们定义对齐动作，这些动作干预情境、用于解释情境的推理或由此产生的框架。通过教育内容生成、创意编码和议论文写作的示例，我们展示了如何将这些动作组合成修复序列，并推导出在运行时支持任务充分概念对齐的设计原则。
英文摘要
The meaning of a concept in use is shaped by the situation, task, goals, and prior knowledge. For example, a request to make a poster "visually appealing for a five-year-old" might evoke bright colors and cartoon imagery for one collaborator, but less text, bold shapes, and visual simplicity for another. We call such task-relevant differences conceptual misalignment. We introduce Alignment Games, a framework for making these differences visible and repairable during human-AI interaction. Drawing on theories of situated conceptualization, we characterize task-specific conceptual frames in terms of relevant attributes, values, relations, constraints, and priorities. We then define alignment moves that intervene on the situation, the reasoning used to interpret it, or the resulting frame. Through examples from educational content generation, creative coding, and argumentative writing, we show how these moves can be composed into repair sequences and derive design principles for supporting task-sufficient conceptual alignment at runtime.
