提出量化评估AI在交通拥堵管理中抗扰与恢复能力的新框架
On the Definition of Robustness and Resilience of AI Agents for Real-time Congestion Management
- 用扰动代理模拟真实与恶意干扰，不改变环境状态
- 通过稳定性和奖励影响衡量鲁棒性，恢复能力评估韧性
- 适用于高风险场景下AI系统安全评估，适合电网智能管控研究者
欧盟《人工智能法案》为高风险领域设定了鲁棒性、弹性与安全性要求，但缺乏具体评估方法。本文提出一种新型框架，用于定量评估强化学习代理在拥堵管理中的鲁棒性与弹性。基于AI友好的数字环境Grid2Op，扰动代理通过修改输入而非改变环境实际状态，模拟自然与对抗性干扰，从而评估AI在多种情景下的表现。鲁棒性通过稳定性与奖励影响指标衡量，弹性则量化性能退化后的恢复能力。实验表明该框架能有效识别漏洞，提升AI在关键应用中的鲁棒性与弹性。
原文摘要 · Abstract (English)
The European Union's Artificial Intelligence (AI) Act defines robustness, resilience, and security requirements for high-risk sectors but lacks detailed methodologies for assessment. This paper introduces a novel framework for quantitatively evaluating the robustness and resilience of reinforcement learning agents in congestion management. Using the AI-friendly digital environment Grid2Op, perturbation agents simulate natural and adversarial disruptions by perturbing the input of AI systems without altering the actual state of the environment, enabling the assessment of AI performance under various scenarios. Robustness is measured through stability and reward impact metrics, while resilience quantifies recovery from performance degradation. The results demonstrate the framework's effectiveness in identifying vulnerabilities and improving AI robustness and resilience for critical applications.
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