Amadeus：当人物模型相遇
原标题：Amadeus: When Models of People Meet
AI 摘要
该论文提出 Amadeus 框架，用国际象棋测试独立学习的人类行为模型能否复现被完全隐藏的真实交互。研究选取 8 位精英棋手，仅用其与目标群体外棋手的对局分别训练个体模型，再组合成对进行合成对弈，并与封存的真实对局比较。评估采用开局族总变差距离和胜平负总变差距离，发现 M1 主要降低 WDL-TV，M2 主要降低开局族 TV，而结合两者的 M3 在两项指标上均保留改进。结果表明独立学习的模型可恢复部分未见交互特征，且不同行为维度的恢复并不互斥。
正文节选
Amadeus: When Models of People Meet Abstract. With the sheer constant advancements raining down in the field of Artificial Intelligence, one particular possibility that may cross our mind is whether it is possible to model agents after humans and, in turn, use these agents to carry out synthetic interactions that predict their real counterparts, or even interactions at a larger scale such as groups or societies. In this paper, we test a more controlled version of this question through chess. We