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：如同许多优化器，贝叶斯优化常因不透明而难以获得用户信任。尽管已有尝试开发以人为中心的优化器，但它们通常假设用户知识是明确无误的，且主要将用户用作优化过程的监督者。我们放宽了这些假设，并借助我们的协作与可解释贝叶斯优化（CoExBO）框架，提出了一种更为平衡的人机协作方式。CoExBO不再要求用户显式提供知识模型，而是采用偏好学习将人类洞察无缝融入优化过程，从而生成与用户偏好相呼应的算法建议。CoExBO在每次迭代中解释其候选选择，以培养信任，使用户对优化过程有更清晰的理解。此外，CoExBO提供无伤害保证，允许用户犯错；即使面临极端的对抗性干预，该算法也能渐近收敛至标准贝叶斯优化。我们通过锂离子电池设计中的人机协作实验验证了CoExBO的有效性，结果表明其相较于传统方法有显著改进。