发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个可解释AI框架，利用图神经网络预测最优控制问题解，并通过可解释算法识别关键变量与约束，在连续搅拌釜反应器案例中验证了预测精度并揭示了活跃的斜坡约束。
AI 中文摘要
在本文中，我们提出了一个可解释的人工智能框架，用于解释通过图神经网络（GNNs）预测的最优控制问题的解。所提出的方法首先训练一个GNN，该GNN针对以图表示的给定实例预测最优控制问题的解。在训练好的GNN基础上，我们使用可解释的人工智能算法来识别影响最优解预测最大的变量、约束、变量-约束连接和参数，即最优控制问题图表示中的节点、边和特征。我们将所提出的方法应用于一个关于连续搅拌釜反应器最优控制的案例研究。首先，结果表明GNN模型能够准确预测操纵变量的最优值。可解释人工智能算法的应用揭示了对于预测最优解最重要的等式和不等式约束。不等式约束对应于斜坡约束，其中一些在最优解处是活跃的。
英文摘要
In this paper, we propose an explainable artificial intelligence framework to explain the solution of optimal control problems predicted with graph neural networks (GNNs). The proposed approach first trains a GNN that predicts the solution of the optimal control problem for a given instance represented as a graph. Given the trained GNN, we use explainable AI algorithms to identify variables, constraints, variable-constraint connections, and parameters, i.e., nodes, edges, and features in the graph representation of the optimal control problem, that affect the prediction of the optimal solution the most. We apply the proposed approach to a case study regarding the optimal control of a continuous stirred tank reactor. First, the results show that the GNN model can accurately predict the optimal values of the manipulated variables. Application of explainable AI algorithms reveals equality and inequality constraints that are the most important for predicting the optimal solution. The inequality constraints correspond to ramping constraints, some of which are active at the optimal solution.
