BO-Muse：一种用于加速实验设计的人类专家与AI协作框架
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
AI总结：
本文提出BO-Muse框架，通过让人类专家主导实验设计、AI注入新颖性打破认知固化，实现昂贵黑盒函数优化，并证明其收敛速度快于单独的AI或人类。
摘要：
本文介绍了BO-Muse，一种用于优化昂贵黑盒函数的人机协作新方法。受提取专家知识并将其蒸馏回AI模型的内在困难，以及对现实世界实验设计中人类行为的观察启发，我们的算法让人类专家在实验过程中发挥主导作用。人类专家可以充分发挥其领域专长，而AI则扮演缪斯角色，注入新颖性并寻找薄弱环节，以打破人类因认知固化导致的过度开发。在温和假设下，我们证明该算法次线性收敛，速度快于单独的AI或人类。我们使用合成数据以及人类专家进行的真实世界实验验证了该算法。
英文摘要：
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.
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。
