Data-driven verification of stochastic linear systems with signal temporal logic constraints

Salamati, Ali; Soudjani, Sadegh

doi:10.1016/j.automatica.2021.109781

Cited by 12 publications

(3 citation statements)

References 37 publications

(80 reference statements)

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“…The authors in [4] further highlight the importance of robustness in system verification. Probably closest to our paper are the works in [31,70,71] and [7,8]. In [31,70,71], the authors combine data-driven and model-based verification techniques to obtain information about the satisfaction probability of a partially known system.…”

Section: Related Workmentioning

confidence: 99%

“…Probably closest to our paper are the works in [31,70,71] and [7,8]. In [31,70,71], the authors combine data-driven and model-based verification techniques to obtain information about the satisfaction probability of a partially known system. The authors in [7,8] present a purely data-driven verification technique to estimate probabilities over robustness distributions of the system.…”

Section: Related Workmentioning

confidence: 99%

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Risk of Stochastic Systems for Temporal Logic Specifications

Lindemann¹,

Jiang²,

Matni³

et al. 2022

Preprint

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Fig. 1. Left: Simulation environment in the autonomous driving simulator CARLA. Middle: Double left turn on which we evaluate four trained neural network lane keeping controllers. Right: Cross-track error 𝑐 𝑒 and orientation error 𝜃 𝑒 used for risk verification of the neural network controllers.The wide availability of data coupled with the computational advances in artificial intelligence and machine learning promise to enable many future technologies such as autonomous driving. While there has been a variety of successful demonstrations of these technologies, critical system failures have repeatedly been reported. Even if rare, such system failures pose a serious barrier to adoption without a rigorous risk assessment. This paper presents a framework for the systematic and rigorous risk verification of systems. We consider a wide range of system specifications formulated in signal temporal logic (STL) and model the system as a stochastic process, permitting discrete-time and continuous-time stochastic processes. We then define the STL robustness risk as the risk of lacking robustness against failure. This definition is motivated as system failures are often caused by missing robustness to modeling errors, system disturbances, and distribution shifts in the underlying data generating process. Within the definition, we permit general classes of risk measures and focus on tail risk measures such as the value-at-risk and the conditional value-at-risk. While the STL robustness risk is in general hard to compute, we propose the approximate STL robustness risk as a more tractable notion that upper bounds the STL robustness risk. We show how the approximate STL robustness risk can accurately be estimated from system trajectory data. For discrete-time stochastic processes, we show under which conditions the approximate STL robustness risk can even be computed exactly. We illustrate our verification algorithm in the autonomous driving simulator CARLA and show how a least risky controller can be selected among four neural network lane keeping controllers for five meaningful system specifications.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Risk of Stochastic Systems for Temporal Logic Specifications

Lindemann¹,

Jiang²,

Matni³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Specifically, we consider specifications described by a fragment of STL formulae, where the horizons of different temporal operators have no overlap. STL formulae are interpreted over continuous time signals and have the advantage of quantitatively evaluating the degree of the satisfaction or violation using robust semantics [10], [11], [14], [15], [17], [22]. The monitor aims to issue alarms when the specification has already or will inevitably be violated.…”

Section: Introductionmentioning

confidence: 99%

Online Monitoring of Dynamic Systems for Signal Temporal Logic Specifications with Model Information

Yu¹,

Dong²,

Yin³

et al. 2022

Preprint

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Online monitoring aims to evaluate or to predict, at runtime, whether or not the behaviors of a system satisfy some desired specification. It plays a key role in safetycritical cyber-physical systems. In this work, we propose a new model-based approach for online monitoring for specifications described by Signal Temporal Logic (STL) formulae. Specifically, we assume that the observed state traces are generated by an underlying dynamic system whose model is known. The main idea is to consider the dynamic of the system when evaluating the satisfaction of the STL formulae. To this end, effective approaches for the computation of feasible sets for STL formulae are provided. We show that, by explicitly utilizing the model information of the dynamic system, the proposed online monitoring algorithm can falsify or certify of the specification in advance compared with existing algorithms, where no model information is used. We also demonstrate the proposed monitoring algorithm by case studies.

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Decoupled Fitness Criteria for Reactive Systems

Egolf,

Tripakis

2023

Lecture Notes in Computer Science

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Data-driven verification of stochastic linear systems with signal temporal logic constraints

Cited by 12 publications

References 37 publications

Risk of Stochastic Systems for Temporal Logic Specifications

Risk of Stochastic Systems for Temporal Logic Specifications

Online Monitoring of Dynamic Systems for Signal Temporal Logic Specifications with Model Information

Decoupled Fitness Criteria for Reactive Systems

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