RouteExplainer：车辆路径问题的解释框架
RouteExplainer: An Explanation Framework for Vehicle Routing Problem
AI总结：
本文提出RouteExplainer，一种事后解释框架，通过将路线视为动作序列并扩展反事实解释，结合边分类器和LLM生成解释，在四种VRP上验证了其快速且合理的准确性。
摘要：
车辆路径问题（VRP）是一个被广泛研究的组合优化问题，并已应用于各种实际问题。虽然VRP的可解释性对于提高实际VRP应用中的可靠性和交互性具有重要意义，但这一领域仍未得到探索。在本文中，我们提出了RouteExplainer，一个事后解释框架，用于解释生成路线中每条边的影响。我们的框架通过将路线重新视为动作序列，并将基于动作影响模型的反事实解释扩展到VRP来实现这一点。为了增强解释，我们还提出了一个边分类器，用于推断每条边的意图，一个用于训练边分类器的损失函数，以及通过大型语言模型（LLMs）生成解释文本。我们在四种不同的VRP上定量评估了我们的边分类器。结果表明，它在保持合理准确性的同时实现了快速计算，从而突显了其在实际应用中部署的潜力。此外，以旅游路线为主题，我们定性评估了由我们的框架生成的解释。这项评估不仅验证了我们的框架，还展示了解释框架与LLMs之间的协同作用。参见https://ntt-dkiku.github.io/xai-vrp获取我们的代码、数据集、模型和演示。
英文摘要：
The Vehicle Routing Problem (VRP) is a widely studied combinatorial optimization problem and has been applied to various practical problems. While the explainability for VRP is significant for improving the reliability and interactivity in practical VRP applications, it remains unexplored. In this paper, we propose RouteExplainer, a post-hoc explanation framework that explains the influence of each edge in a generated route. Our framework realizes this by rethinking a route as the sequence of actions and extending counterfactual explanations based on the action influence model to VRP. To enhance the explanation, we additionally propose an edge classifier that infers the intentions of each edge, a loss function to train the edge classifier, and explanation-text generation by Large Language Models (LLMs). We quantitatively evaluate our edge classifier on four different VRPs. The results demonstrate its rapid computation while maintaining reasonable accuracy, thereby highlighting its potential for deployment in practical applications. Moreover, on the subject of a tourist route, we qualitatively evaluate explanations generated by our framework. This evaluation not only validates our framework but also shows the synergy between explanation frameworks and LLMs. See https://ntt-dkiku.github.io/xai-vrp for our code, datasets, models, and demo.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
