arXiv cs.AI· Zhipeng Cao, Taoran Wu, Weiru Cui, Luke Ong, Bai Xue·· 12 小时前AI 评分18
学习型屏障证书的任意时间有效运行时监控
Anytime-Valid Runtime Monitoring of Learned Barrier Certificates
AI 导读
研究提出一种任意时间有效的运行时监控器，利用观测到的闭环轨迹审计学习型屏障证书，将条件屏障漂移条件建模为逐回合序贯假设检验，并把屏障增量转化为非负检验超鞅，从而在任意停止时间下实现有限样本误报控制。该监控器还引入监测边界的确定性时间整形，在部署初期更保守且保持任意时间有效性，其目标是检测证书与观测动态不再一致的统计证据，而非预测不安全进入。代码与数据已公开。
正文 · 原文
Abstract:Learned barrier certificates provide a principled mechanism for establishing safety of stochastic systems, but their validity may deteriorate during deployment when the underlying dynamics differ from those used for training. We propose an anytime-valid runtime monitor that audits a learned barrier certificate using observed closed-loop trajectories. The monitor is intended to detect loss of statistical support for the deployed barrier condition; it does not by itself guarantee detection before unsafe entry or safety of a subsequent intervention. Specifically, the conditional barrier-drift condition is formulated as an episode-wise sequential hypothesis test, and observed barrier increments are transformed into a nonnegative test supermartingale, yielding finite-sample false-alarm control under arbitrary stopping times. Inspired by finite-horizon safety verification, we further introduce a deterministic temporal shaping of the monitoring boundary, making the monitor more conservative early in a prescribed deployment horizon while preserving anytime-validity. The monitor is designed to detect statistical evidence that the safety certificate is no longer consistent with the observed dynamics, rather than to predict unsafe entry. To assess the monitor empirically, we evaluate empirical alarm behavior under valid and invalid certificates. Code and data are available at this https URL.
| Comments: | 17 pages, 7 figures. An earlier version of this work was submitted to AAAI 2027 on 21 July 2026 (submission no. 28239). The core monitoring framework and main theoretical results were already present in that submission |
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC); Statistics Theory (math.ST) |
| MSC classes: | 68Q60 (Primary), 68T05, 68T07, 93C10, 93C55, 93E03, 93E10, 62L10, 60G42 (Secondary) |
| ACM classes: | I.2.6; I.2.8; F.3.1; D.2.4; G.3 |
| Cite as: | arXiv:2610.27726 [eess.SY] |
| (or arXiv:2610.27726v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27726 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhipeng Cao [view email]
[v1]
Tue, 20 Oct 2026 06:46:22 UTC (580 KB)
来源：arXiv cs.AI · arxiv.org