In industry, Bayesian optimization (BO) is widely applied in the human-AI collaborative parameter tuning of cyber-physical systems. However, BO's solutions may deviate from human experts' actual goal due to approximation errors and simplified objectives, requiring subsequent tuning. The black-box nature of BO limits the collaborative tuning process because the expert does not trust the BO recommendations. Current explainable AI (XAI) methods are not tailored for optimization and thus fall short of addressing this gap. To bridge this gap, we propose TNTRules (TUNE-NOTUNE Rules), a post-hoc, rule-based explainability method that produces high quality explanations through multiobjective optimization. Our evaluation of benchmark optimization problems and real-world hyperparameter optimization tasks demonstrates TNTRules' superiority over state-of-the-art XAI methods in generating high quality explanations. This work contributes to the intersection of BO and XAI, providing interpretable optimization techniques for real-world applications.

翻译：在工业领域，贝叶斯优化（Bayesian Optimization, BO）被广泛用于信息物理系统中的人机协同参数调优。然而，由于近似误差和目标简化，BO的解可能偏离人类专家的实际目标，需要后续调整。BO的黑箱特性限制了协同调优过程，因为专家不信任BO的推荐结果。现有可解释人工智能（XAI）方法并非专为优化设计，因此无法弥合这一差距。为填补这一空白，我们提出TNTRules（TUNE-NOTUNE Rules），一种基于规则的事后可解释性方法，通过多目标优化生成高质量解释。对基准优化问题和实际超参数优化任务的评估表明，TNTRules在生成高质量解释方面优于最先进的XAI方法。本工作推动了BO与XAI的交叉领域发展，为实际应用提供了可解释的优化技术。