In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions. Inspired by the intrinsic difficulty of extracting expert knowledge and distilling it back into AI models and by observations of human behavior in real-world experimental design, our algorithm lets the human expert take the lead in the experimental process. The human expert can use their domain expertise to its full potential, while the AI plays the role of a muse, injecting novelty and searching for areas of weakness to break the human out of over-exploitation induced by cognitive entrenchment. With mild assumptions, we show that our algorithm converges sub-linearly, at a rate faster than the AI or human alone. We validate our algorithm using synthetic data and with human experts performing real-world experiments.

翻译：本文提出BO-Muse，一种用于优化昂贵黑箱函数的人机协作新方法。受专家知识提取与回馈至AI模型的固有困难，以及真实实验设计中人类行为观察的启发，该算法让人类专家主导实验过程。人类专家可充分发挥其领域专长，而AI扮演"缪斯"角色，注入新颖性并搜索薄弱区域，以打破人类因认知固化导致的过度利用倾向。在温和假设下，我们证明该算法以快于单独使用AI或人类的速度实现次线性收敛。我们通过合成数据及人类专家开展的真实实验对算法进行了验证。