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. Code is available https://github.com/ma921/CoExBO.

翻译：如同许多优化器一样，贝叶斯优化常因黑箱问题难以获得用户信任。尽管已有尝试开发以人为中心的优化器，但这些方法通常假设用户知识明确且无误差，仅将用户用作优化过程的监督者。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）。CoExBO无需用户显式提供知识模型，而是通过偏好学习将人类见解无缝融入优化过程，从而生成与用户偏好契合的算法建议。CoExBO在每个迭代中解释其候选选择机制以增强信任，使用户能更清晰把握优化过程。此外，CoExBO提供无损害保证，允许用户犯错：即便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。通过锂离子电池设计中人机协作实验，我们验证了CoExBO的有效性，结果显示其相较传统方法有显著改进。代码见https://github.com/ma921/CoExBO。