为数学优化决策提供与模型结构一致的可解释性分析。
Coherent Local Explanations for Mathematical Optimization
- 基于采样方法生成与优化问题结构一致的局部解释。
- 可解释目标值和决策变量，适用于精确与启发式算法。
- 在最短路径、背包和车辆路径问题中验证有效性。
可解释人工智能的发展旨在提升机器学习模型的透明度。与此同时，对复杂数学优化算法决策的解释需求日益增长。然而，现有解释方法未考虑底层优化问题的结构，导致结果不可靠。为此，我们提出数学优化的一致局部解释方法（CLEMO），能够对优化模型的多个组件——目标值和决策变量——提供与模型结构一致的解释。该方法基于采样，适用于精确解法与启发式算法。通过最短路径问题、背包问题和车辆路径问题的实验，验证了CLEMO的有效性。
原文摘要 · Abstract (English)
The surge of explainable artificial intelligence methods seeks to enhance transparency and explainability in machine learning models. At the same time, there is a growing demand for explaining decisions taken through complex algorithms used in mathematical optimization. However, current explanation methods do not take into account the structure of the underlying optimization problem, leading to unreliable outcomes. In response to this need, we introduce Coherent Local Explanations for Mathematical Optimization (CLEMO). CLEMO provides explanations for multiple components of optimization models, the objective value and decision variables, which are coherent with the underlying model structure. Our sampling-based procedure can provide explanations for the behavior of exact and heuristic solution algorithms. The effectiveness of CLEMO is illustrated by experiments for the shortest path problem, the knapsack problem, and the vehicle routing problem.
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