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：与许多优化器类似，贝叶斯优化常因缺乏透明度而难以获得用户信任。尽管已有研究致力于开发以人为中心的优化器，但它们通常假设用户知识具有明确规范且无错误，主要将用户作为优化过程的监督者。我们放宽这些假设，提出更均衡的人机协作框架——协同与可解释贝叶斯优化（CoExBO）。该框架不要求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，使算法建议与用户偏好产生共鸣。CoExBO每次迭代都会解释其候选方案的选择逻辑以增强信任，帮助用户更清晰地理解优化过程。此外，CoExBO提供无伤害保障机制：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性能。通过在锂离子电池设计领域进行的人机协同实验，我们验证了CoExBO的有效性，其性能较传统方法实现显著提升。