Hybrid System Falsification Under (In)equality Constraints via Search Space Transformation

Zhang, Zhenya; Arcaini, Paolo; Hasuo, Ichiro

doi:10.1109/tcad.2020.3013073

Cited by 15 publications

(4 citation statements)

References 29 publications

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“…So far, the falsification problem has received extensive industrial and academic attention. One possible approach direction by hill-climbing optimization is an established field, too: see [2][3][4]10,[13][14][15]17,26,29,[36][37][38][39]42] and the tools Breach [13] and S-TaLiRo [4]. We formulate the problem and the methodology, for later use in describing our falsification approach.…”

Section: Hill Climbing-guided Falsificationmentioning

confidence: 99%

Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness

Zhang

Lyu

Arcaini

et al. 2021

Lecture Notes in Computer Science

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Hybrid system falsification is an important quality assurance method for cyber-physical systems with the advantage of scalability and feasibility in practice than exhaustive verification. Falsification, given a desired temporal specification, tries to find an input of violation instead of a proof guarantee. The state-of-the-art falsification approaches often employ stochastic hill-climbing optimization that minimizes the degree of satisfaction of the temporal specification, given by its quantitative robust semantics. However, it has been shown that the performance of falsification could be severely affected by the so-called scale problem, related to the different scales of the signals used in the specification (e.g., rpm and speed): in the robustness computation, the contribution of a signal could be masked by another one. In this paper, we propose a novel approach to tackle this problem. We first introduce a new robustness definition, called QB-Robustness, which combines classical Boolean satisfaction and quantitative robustness. We prove that QB-Robustness can be used to judge the satisfaction of the specification and avoid the scale problem in its computation. QB-Robustness is exploited by a falsification approach based on Monte Carlo Tree Search over the structure of the formal specification. First, tree traversal identifies the sub-formulas for which it is needed to compute the quantitative robustness. Then, on the leaves, numerical hill-climbing optimization is performed, aiming to falsify such sub-formulas. Our in-depth evaluation on multiple benchmarks demonstrates that our approach achieves better falsification results than the state-of-the-art falsification approaches guided by the classical quantitative robustness, and it is largely not affected by the scale problem.

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Section: Hill Climbing-guided Falsificationmentioning

confidence: 99%

Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness

Zhang

Lyu

Arcaini

et al. 2021

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Software tools, including S-TaLiRo, Breach, C2E2, and DryVR, have become instrumental in the field of system falsification, representing crucial advancements in simulation-driven testing (Fainekos et al 2012;Annpureddy et al 2011;Donzé 2010;Duggirala et al 2015;Qi et al 2018). These tools are part of a broader evolution that encompasses techniques ranging from searchbased algorithms to machine learning and data-driven approaches, all aimed at enhancing the effectiveness of falsification methods (Ramezani et al 2021;Zhang et al 2021;Zhang, Arcaini, and Hasuo 2020;Deshmukh et al 2015;Ernst et al 2021;Mathesen, Pedrielli, and Fainekos 2021;Akazaki et al 2018;Qin et al 2019;Zhang, Hasuo, and Arcaini 2019). Despite these advancements, the challenges of simulation-driven falsification remain multifaceted.…”

Section: Introductionmentioning

confidence: 99%

Exploring the role of simulator fidelity in the safety validation of learning‐enabled autonomous systems

Baheri

2023

AI Magazine

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This article presents key insights from the New Faculty Highlights talk given at AAAI 2023, focusing on the crucial role of fidelity simulators in the safety evaluation of learning‐enabled components (LECs) within safety‐critical systems. With the rising integration of LECs in safety‐critical systems, the imperative for rigorous safety and reliability verification has intensified. Safety assurance goes beyond mere compliance, forming a foundational element in the deployment of LECs to reduce risks and ensure robust operation. In this evolving field, simulations have become an indispensable tool, and fidelity's role as a critical parameter is increasingly recognized. By employing multifidelity simulations that balance the needs for accuracy and computational efficiency, new paths toward comprehensive safety validation are emerging. This article delves into our recent research, emphasizing the role of simulation fidelity in the validation of LECs in safety‐critical systems.

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“…The primary goal of falsification is to pinpoint scenarios that could lead to system failure or breaches in safety specifications. Various techniques have been proposed to tackle the falsification problem, including optimization-based methods [11,19,6], search-based algorithms [12,15,20], and reinforcement learning approaches [18,16,10]. These methods strive to efficiently explore the state and parameter space in order to discover potential failure scenarios, ultimately enabling system design refinement and enhanced safety assurances.…”

Section: Introductionmentioning

confidence: 99%

A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems

Baheri

Ren

Johnson

et al. 2022

AIAA AVIATION 2022 Forum

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Safety validation is a crucial component in the development and deployment of autonomous systems, such as self-driving vehicles and robotic systems. Ensuring safe operation necessitates extensive testing and verification of control policies, typically conducted in simulation environments. High-fidelity simulators accurately model real-world dynamics but entail high computational costs, limiting their scalability for exhaustive testing. Conversely, low-fidelity simulators offer efficiency but may not capture the intricacies of high-fidelity simulators, potentially yielding false conclusions. We propose a joint falsification and fidelity optimization framework for safety validation of autonomous systems. Our mathematical formulation combines counterexample searches with simulator fidelity improvement, facilitating more efficient exploration of the critical environmental configurations challenging the control system. Our contributions encompass a set of theorems addressing counterexample sensitivity analysis, sample complexity, convergence, the interplay between the outer and inner optimization loops, and regret bound analysis. The proposed joint optimization approach enables a more targeted and efficient testing process, optimizes the use of available computational resources, and enhances confidence in autonomous system safety validation.

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Hybrid System Falsification Under (In)equality Constraints via Search Space Transformation

Cited by 15 publications

References 29 publications

Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness

Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness

Exploring the role of simulator fidelity in the safety validation of learning‐enabled autonomous systems

A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems

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