Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced human-AI partnership with our Collaborative and Explainable Bayesian Optimization (CoExBO) framework. Instead of explicitly requiring a user to provide a knowledge model, CoExBO employs preference learning to seamlessly integrate human insights into the optimization, resulting in algorithmic suggestions that resonate with user preference. CoExBO explains its candidate selection every iteration to foster trust, empowering users with a clearer grasp of the optimization. Furthermore, CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with extreme adversarial interventions, the algorithm converges asymptotically to a vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI teaming experiments in lithium-ion battery design, highlighting substantial improvements over conventional methods.

翻译：与许多优化器一样，贝叶斯优化常因过程不透明而难以赢得用户信任。尽管已有研究致力于开发以人为中心的优化器，但此类方法通常假设用户知识是精确且无误差的，并将用户主要视为优化过程的监督者。我们放宽这些假设，提出更平衡的人机协作框架——协作与可解释贝叶斯优化（CoExBO）。不同于显式要求用户提供知识模型，CoExBO通过偏好学习将人类洞察无缝融入优化过程，使算法建议与用户偏好相吻合。CoExBO每次迭代都会解释其候选选择，通过增强用户对优化过程的清晰理解来培养信任。此外，CoExBO提供无伤害保障机制，允许用户犯错——即使面对极端对抗性干预，算法也能渐近收敛至标准贝叶斯优化。通过锂电池设计领域的人机协作实验，我们验证了CoExBO的有效性，证明其相较传统方法具有显著改进。