Querying Parametric Temporal Logic Properties on Embedded Systems

Yang, Hengyi; Hoxha, Bardh; Fainekos, Georgios

doi:10.1007/978-3-642-34691-0_11

Cited by 40 publications

(37 citation statements)

References 25 publications

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“…It may be worthwhile to see if automata-based mining could be adapted to the hybrid systems domain. The work closest to the proposed approach appears in [33], in which the authors introduce Parametric MTL (PMTL), which adds a single time or scale parameter to MTL formulas. This parameter is then estimated using stochastic optimization within S-Taliro.…”

Section: Related Workmentioning

confidence: 99%

Mining Requirements From Closed-Loop Control Models

Jin¹,

Donzé

Deshmukh³

et al. 2015

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

157

151

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A significant challenge to the formal validation of softwarebased industrial control systems is that system requirements are often imprecise, non-modular, evolving, or even simply unknown. We propose a framework for mining requirements from the closed-loop model of an industrial-scale control system, such as one specified in the Simulink modeling language. The input to our algorithm is a requirement template expressed in Parametric Signal Temporal Logic -a formalism to express temporal formulas in which concrete signal or time values are replaced by parameters. Our algorithm is an instance of counterexample-guided inductive synthesis: an intermediate candidate requirement is synthesized from simulation traces of the system, which is refined using counterexamples to the candidate obtained with the help of a falsification tool. The algorithm terminates when no counterexample is found. Mining has many usage scenarios: mined requirements can be used to validate future modifications of the model, they can be used to enhance understanding of legacy models, and can also guide the process of bug-finding through simulations. We present two case studies for requirement mining: a simple automobile transmission controller and an industrial airpath control model for an engine.

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Section: Related Workmentioning

confidence: 99%

Mining Requirements From Closed-Loop Control Models

Jin¹,

Donzé

Deshmukh³

et al. 2015

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

157

151

View full text Add to dashboard Cite

show abstract

“…In [19], the authors use relations such as after, before, begins and includes for two given events. In [8,28,21], the use of temporal logic, such as modal temporal logic, reified temporal logic, metric temporal logic and linear temporal logic, is shown to reason about temporal relations. However, the focus of these approaches is to find a temporal ordering of logical events, and they do not consider important quantitative parameters such as time intervals and spatial metrics.…”

Section: Related Workmentioning

confidence: 99%

“…Human readability is important because it can provide intuition into the internal reasoning of the classifier, which in turn can help non-expert users to gain a qualitative and quantitative understanding of the classification output. Recent work in temporal logic inference exploits the expressivity of temporal logic in modelling such a time series classifier [6,13,28,21]. However, these frameworks are too computationally expensive to handle large datasets and may not run on mobile robots with limited resources, or expressivity is limited due to their logical structure.…”

Section: Introductionmentioning

confidence: 99%

Rich Time Series Classification Using Temporal Logic

Yoo

Belta

2017

Robotics: Science and Systems XIII

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Abstract-Time series classification is an important task in robotics that is often solved using supervised machine learning. However, classifier models are typically not 'readable' in the sense that humans cannot intuitively learn useful information about the relationship between inputs and outputs. In this paper, we address the problem of rich time series classification where we propose a novel framework for finding a temporal logic classifier specified in a human-readable form. The classifier is represented as a signal temporal logic (STL) formula that is expressive in capturing spatial, temporal and logical relations from a continuous-valued dataset over time. In the framework, we first find a set of representative logical formulas from the raw dataset, and then construct an STL classifier using a treebased clustering algorithm. We show that the framework runs in polynomial time and validate it using simulated examples where our framework is significantly more efficient than the closest existing framework (up to 920 times faster).

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“…The case study results in this paper were generated with this updated software. a) Related Work: Most of the recent research on logical inference, the problem of inferring from data a logical expression that describes system properties, has focused on the estimation of parameters associated with a given temporal logic structure [2], [3], [15], [33]. That is, a designer gives a structure such as "The speed settles below v m/s within τ seconds" as an input and the inference procedure finds optimal values for v and τ .…”

Section: Introductionmentioning

confidence: 99%

“…Even though SA converges in probability to the global optimum, there is no theoretical proof on the convergence rate and how far the solution is from the global optimum after a number of iterations. Some promising avenues include utilizing the monotonicity of the robustness function r(s, φ θ ) with respect to θ [15], [33] and using cross-entropy method guided by robustness degree to sample inputs [25].…”

mentioning

confidence: 99%

Temporal Logics for Learning and Detection of Anomalous Behavior

Kong

Jones

Belta

2017

IEEE Trans. Automat. Contr.

106

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Abstract-The increased complexity of modern systems necessitates automated anomaly detection methods to detect possible anomalous behavior determined by malfunctions or external attacks. We present formal methods for inferring (via supervised learning) and detecting (via unsupervised learning) anomalous behavior. Our procedures use data to construct a signal temporal logic (STL) formula that describes normal system behavior. This logic can be used to formulate properties such as "If the train brakes within 500 m of the platform at a speed of 50 km/hr, then it will stop in at least 30 s and at most 50 s." Our procedure infers not only the physical parameters involved in the formula (e.g., 500 m in the example above) but also its logical structure. STL gives a more human-readable representation of behavior than classifiers represented as surfaces in high-dimensional feature spaces. The learned formula enables us to perform early detection by using monitoring techniques and anomaly mitigation by using formal synthesis techniques. We demonstrate the power of our methods with examples of naval surveillance and a train braking system.

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Querying Parametric Temporal Logic Properties on Embedded Systems

Cited by 40 publications

References 25 publications

Mining Requirements From Closed-Loop Control Models

Mining Requirements From Closed-Loop Control Models

Rich Time Series Classification Using Temporal Logic

Temporal Logics for Learning and Detection of Anomalous Behavior

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