SHF: Small: Premonition: A Methodology for Predictive Monitoring with Probabilistic Guarantees

SHF：小：预感：具有概率保证的预测监测方法

基本信息

批准号：
1910088
负责人：
Jyotirmoy Deshmukh
金额：
$ 50万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910088&HistoricalAwards=false
关键词：
SHF Small Premonition Methodology Predictive

项目摘要

Autonomous systems such as unmanned aerial vehicles, medical devices and self-driving cars are challenging to design because of the inherent software complexity, as well as uncertainty in their operating environment. Unfortunately, many of these autonomous systems are also safety-critical; there are real tragic implications on human life and property when these systems malfunction. Monitoring the safety of such autonomous cyber-physical system while they are operating has long been viewed as scalable solution to ensure safety of these systems. Such safety properties can often be written using logical formalisms such as Signal Temporal Logic (STL). The project develops a modular framework called Premonition for monitoring temporal-logic properties of autonomous cyber-physical systems. The following abilities of Premonition framework highlight its novelty: (1) Predictive monitoring to forecast the failure of a safety property before a violation actually occurs; (2) Resource-awareness for monitoring that is performed with low memory and sensing overhead; and finally, 3) Probabilistic guarantees for providing quantifiable bounds on the accuracy of the predictions made.The Premonition framework is highly interdisciplinary: it combines statistical methods for predicting future values of time-series data with techniques from formal methods. The key site for innovation is in new techniques for obtaining probabilistic guarantees on whether a safety property will be violated by the system in the future. Such guarantees are obtained for different kinds of system models. For data-driven system models, Premonition weaves prior work on forecasting for stochastic processes with monitoring algorithms for STL. For dynamical system models, the framework introduces new algorithms to monitor STL properties on over-approximations of future reachable states of the system. For system models with explicit uncertainty models, the framework focuses on resource-aware predictive monitoring. For systems with unobservable states, it provides new algorithms for latent- xstate predictive monitoring, i.e. algorithms that give probabilistic guarantees on whether the hidden system states violate a given property in the future, by monitoring the observable states of the system. Probabilistic guarantees are useful for designing enforcement and warning mechanisms to provide safety assurance for systems. This project's impacts are in making dynamic safety-assurance cases for autonomous systems such as automated insulin-delivery systems, unmanned aerial vehicles and self-driving cars.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于固有的软件复杂性以及操作环境的不确定性，无人机、医疗设备和自动驾驶汽车等自主系统的设计具有挑战性。不幸的是，许多自主系统也对安全至关重要。当这些系统发生故障时，会对人类生命和财产造成真正的悲剧性影响。长期以来，对此类自主网络物理系统运行时的安全性进行监控一直被视为确保这些系统安全的可扩展解决方案。此类安全属性通常可以使用信号时序逻辑 (STL) 等逻辑形式来编写。该项目开发了一个名为 Premonition 的模块化框架，用于监控自主网络物理系统的时间逻辑属性。 Premonition框架的以下功能凸显了其新颖性：（1）预测性监控，在违规实际发生之前预测安全属性的故障； (2) 资源感知，以低内存和感知开销执行监控；最后，3）为预测的准确性提供可量化界限的概率保证。Premonition 框架是高度跨学科的：它将预测时间序列数据未来值的统计方法与形式方法中的技术相结合。创新的关键在于新技术，以获得系统未来是否会违反安全属性的概率保证。对于不同类型的系统模型都可以获得这样的保证。对于数据驱动的系统模型，Premonition 将之前的随机过程预测工作与 STL 监控算法结合起来。对于动态系统模型，该框架引入了新算法来监控系统未来可达状态的过度近似的 STL 属性。对于具有明确不确定性模型的系统模型，该框架侧重于资源感知预测监控。对于具有不可观察状态的系统，它提供了用于潜在状态预测监控的新算法，即通过监控系统的可观察状态，对隐藏系统状态未来是否违反给定属性提供概率保证的算法。概率保证可用于设计执行和警告机制，为系统提供安全保证。该项目的影响在于为自动化系统（例如自动胰岛素输送系统、无人机和自动驾驶汽车）制定动态安全保证案例。该奖项反映了 NSF 的法定使命，并通过使用基金会的知识进行评估，被认为值得支持。优点和更广泛的影响审查标准。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Conformal Prediction for STL Runtime Verification

DOI：
10.1145/3576841.3585927
发表时间：
2022-11
期刊：
2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
影响因子：
0
作者：
Lars Lindemann;Xin Qin;Jyotirmoy V. Deshmukh;George Pappas
通讯作者：
Lars Lindemann;Xin Qin;Jyotirmoy V. Deshmukh;George Pappas

Robust Testing for Cyber-Physical Systems using Reinforcement Learning

DOI：
10.1145/3610579.3611087
发表时间：
2023-09
期刊：
2023 21st ACM-IEEE International Symposium on Formal Methods and Models for System Design (MEMOCODE)
影响因子：
0
作者：
Xin Qin;Nikos Aréchiga;Jyotirmoy V. Deshmukh;Andrew Best
通讯作者：
Xin Qin;Nikos Aréchiga;Jyotirmoy V. Deshmukh;Andrew Best

Clairvoyant Monitoring for Signal Temporal Logic

信号时间逻辑的透视监测