基于解释的运行时验证用于可信的机器学习驱动的光网络
Explanation-Based Runtime Verification for Trustworthy ML-driven Optical Networks
AI总结：
研究将机器学习模型用于光网络自动化时决策可靠性问题，提出基于解释的运行时验证方法，利用模型解释评估决策合理性，在光路径传输质量分类用例中验证该方法有效，能拦截错误决策并保持高自动化率。
AI中文摘要：
机器学习模型越来越多地集成到光网络自动化框架中，以支持诸如故障管理、性能监测和资源分配等任务。在这些环境中，机器学习驱动的预测可能直接与控制平面动作耦合，错误决策会立即影响服务质量、资源效率和网络稳定性。随着自动化水平提高，确保部署时单个决策的可靠性至关重要。可解释人工智能技术已出现以提高透明度。本文介绍基于解释的运行时验证方法，利用模型解释在网络控制回路中执行前评估单个机器学习决策的合理性。该方法在运行时评估解释一致性和物理基础一致性，使系统能推迟或拒绝标记为不确定的决策。我们在光路径传输质量分类的代表性用例上展示了该方法的有效性。实验结果表明，基于解释的验证能拦截很大一部分错误决策，同时保持高自动化率。
英文摘要：
Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, performance monitoring and resource allocation. In these environments, ML-driven predictions may be directly coupled with control-plane actions where incorrect decisions can immediately impact service quality, resource efficiency, and network stability. As automation levels increase, ensuring the reliability of individual decisions at deployment time becomes a critical requirement. Explainable artificial intelligence (XAI) techniques have emerged to improve transparency by highlighting the factors influencing ML predictions. In addition to identifying influential features, they provide insights into the underlying reasoning process of the model, revealing how different input variables contribute to the final outcome and how feature interactions shape the decision boundary. In this work, we introduce explanation-based runtime verification, an approach that exploits model explanations to assess the soundness of individual ML decisions before they are executed in the network control loop. The proposed approach evaluates explanation coherence and physics grounding consistency at runtime, enabling the system to defer or reject decisions flagged as uncertain. We demonstrate the effectiveness of our approach on a representative use case of lightpath quality of transmission classification. Experimental results show that explanation-based verification can intercept a significant fraction of erroneous decisions while preserving high automation rate.
