Functional gradient descent method for Metric Temporal Logic specifications

Abbas, Houssam; Winn, Andrew K.; Fainekos, Georgios; Julius, A. Agung

doi:10.1109/acc.2014.6859453

Cited by 35 publications

(38 citation statements)

References 24 publications

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“…For example, one can consider different optimization algorithms to search for the counterexample (e.g. ant colony optimization [9] or functional gradient descent [10]). Falsification requires use of a formal specification, typically written in Metric Interval Temporal Logic (MITL) [11] or Signal Temporal Logic (STL) [12] (or some variant thereof).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Temporal Logic Falsification With Specification Transformation and Valued Booleans

Eddeland

Claessen

Smallbone

et al. 2020

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

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Cyber-Physical Systems (CPSs) are systems with both physical and software components, for example cars and industrial robots. Since these systems exhibit both discrete and continuous dynamics, they are complex and it is thus difficult to verify that they behave as expected. Falsification of temporal logic properties is an approach to find counterexamples to CPSs by means of simulation. In this paper, we propose two additions to enhance the capability of falsification and make it more viable in a large-scale industrial setting. The first addition is a framework for transforming specifications from a signal-based model into Signal Temporal Logic. The second addition is the use of Valued Booleans and an additive robust semantics in the falsification process. We evaluate the performance of the additive robust semantics on a set of benchmark models, and we can see that which semantics are preferable depend both on the model and on the specification. arXiv:1910.08306v1 [eess.SY]

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Section: Introductionmentioning

confidence: 99%

Enhancing Temporal Logic Falsification With Specification Transformation and Valued Booleans

Eddeland

Claessen

Smallbone

et al. 2020

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

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“…Our method in this paper is a SS approach that uses optimization and robustness metric to solve the falsification problem. In [23,14] robustness-based falsification is guided using descent direction; however, that line of work is only applicable to purely continuous systems. In [31], descent direction is calculated in the case of linear hybrid systems using optimization methods.…”

Section: Motion Planning Approaches Such As Rapidly-exploring Random mentioning

confidence: 99%

Local Descent for Temporal Logic Falsification of Cyber-Physical Systems

Yaghoubi

Fainekos

2019

Cyber Physical Systems. Design, Modeling, and Evaluation

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One way to analyze Cyber-Physical Systems is by modeling them as hybrid automata. Since reachability analysis for hybrid nonlinear automata is a very challenging and computationally expensive problem, in practice, engineers try to solve the requirements falsification problem. In one method, the falsification problem is solved by minimizing a robustness metric induced by the requirements. This optimization problem is usually a non-convex non-smooth problem that requires heuristic and analytical guidance to be solved. In this paper, functional gradient descent for hybrid systems is utilized for locally decreasing the robustness metric. The local descent method is combined with Simulated Annealing as a global optimization method to search for unsafe behaviors.

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“…e assumption of local continuity can be proven in many situations including for nonlinear di erential equations and switched systems under time-triggered switching [1,3].…”

Section: Falsifying Neighborhoodsmentioning

confidence: 99%

Analyzing neighborhoods of falsifying traces in cyber-physical systems

Diwakaran

Sankaranarayanan

Trivedi

2017

Proceedings of the 8th International Conference on Cyber-Physical Systems

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We study the problem of analyzing falsifying traces of cyber-physical systems. Speci cally, given a system model and an input which is a counterexample to a property of interest, we wish to understand which parts of the inputs are "responsible" for the counterexample as a whole. Whereas this problem is well known to be hard to solve precisely, we provide an approach based on learning from repeated simulations of the system under test.Our approach generalizes the classic concept of "one-at-a-time" sensitivity analysis used in the risk and decision analysis community to understand how inputs to a system in uence a property in question. Speci cally, we pose the problem as one of nding a neighborhood of inputs that contains the falsifying counterexample in question, such that each point in this neighborhood corresponds to a falsifying input with a high probability. We use ideas from statistical hypothesis testing to infer and validate such neighborhoods from repeated simulations of the system under test.is approach not only helps to understand the sensitivity of these counterexamples to various parts of the inputs, but also generalizes or widens the given counterexample by returning a neighborhood of counterexamples around it.We demonstrate our approach on a series of increasingly complex examples from automotive and closed loop medical device domains. We also compare our approach against related techniques based on regression and machine learning. CCS CONCEPTS•Computer systems organization →Embedded and cyberphysical systems; •Mathematics of computing →Hypothesis testing and con dence interval computation;

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Functional gradient descent method for Metric Temporal Logic specifications

Cited by 35 publications

References 24 publications

Enhancing Temporal Logic Falsification With Specification Transformation and Valued Booleans

Enhancing Temporal Logic Falsification With Specification Transformation and Valued Booleans

Local Descent for Temporal Logic Falsification of Cyber-Physical Systems

Analyzing neighborhoods of falsifying traces in cyber-physical systems

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