发表机构
University of Strathclyde; Weir（斯特拉斯克莱德大学; 威尔）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对工业过程控制优化中算法设计者与操作员的信任问题，提出结合隐函数定理灵敏度分析、SHAP归因及大语言模型叙述生成的方法，在HPGR控制优化问题上实现等效SHAP归因且加速超40倍，能实时生成自然语言解释。
AI 中文摘要
自动化优化在工业过程中越来越普遍，但算法设计者和执行建议的操作员之间存在信任差距。像SHAP这样的可解释人工智能方法改变了机器学习预测的可解释性，优化输出也能从类似技术中受益。本文提出一种将基于隐函数定理（IFT）的灵敏度分析与通过大语言模型（LLM）的SHAP归因和叙述生成相结合的方法，为操作员量身定制解释。该方法利用IFT从最优条件计算精确的参数灵敏度∂p*/∂x，实现高效的GradientSHAP计算。对于一个具有22个特征的工业高压辊磨机（HPGR）控制优化问题，实现了等效的SHAP归因（与KernelSHAP的相关性>0.99），加速超过40倍，能够进行实时自然语言解释。我们在工业场景中进行了验证，并展示了领域专家对生成解释的反馈。
英文摘要
Automated optimisation is increasingly adopted in industrial processes, yet a trust gap persists between engineers who design these algorithms and operators who must act on their recommendations. Explainable AI methods like SHAP (SHapley Additive exPlanations) have transformed interpretability for machine learning predictions; optimisation outputs could benefit from similar techniques. We present an approach that integrates Implicit Function Theorem (IFT) based sensitivity analysis with SHAP attribution and narrative generation via Large Language Models (LLM), producing explanations tailored for operators. Our approach leverages IFT to compute exact parameter sensitivities $\partial p^*/\partial x$ from the optimality conditions, enabling efficient GradientSHAP computation. For an industrial High Pressure Grinding Roll (HPGR) control optimisation problem with 22 features, we achieve equivalent SHAP attributions (correlation $>$0.99 with KernelSHAP) with over 40$\times$ speedup, enabling real-time natural language explanations. We validate on industrial scenarios and present feedback from domain experts on generated explanations.
