Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and could greatly benefit from the expertise of its human (domain) experts who often reason about systems at different abstraction levels using physical properties that are not necessarily directly measured (or measurable). In this paper, we propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into the surrogate modeling to further boost the performance of BO. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments. We discuss the convergence behavior of our proposed framework. Our experimental results involving synthetic functions and real-world datasets show the superiority of our method against the baselines.

翻译：实验（设计）优化是设计和发现新产品与工艺的关键驱动力。贝叶斯优化（BO）作为一种高效工具，常用于优化代价高昂且具有黑箱特性的实验设计流程。尽管贝叶斯优化作为基于原则的数据驱动方法适用于实验优化，但其需从零开始学习，未能充分利用领域专家对系统不同抽象层次的推理能力——这些专家常借助未必可直接测量（或可量化）的物理属性进行推理。本文提出一种人机协作贝叶斯框架，通过将专家对未测量抽象属性的偏好融入代理建模，进一步提升贝叶斯优化的性能。我们提供了一种高效策略，可同时处理偏好判断中可能存在的错误/误导性专家偏差，并探讨了所提框架的收敛性质。针对合成函数与真实数据集开展的实验结果表明，本方法在性能上优于现有基准算法。