HyMn: Mining linear hybrid automata from input output traces of cyber-physical systems

Lamrani, Imane; Banerjee, Ayan; Gupta, Sandeep

doi:10.1109/icphys.2018.8387670

Cited by 17 publications

(7 citation statements)

References 3 publications

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“…They derive an automaton based on a Mealy inference algorithm within LearnLib. [31], where candidate models are clustered from traces using feature vectors, and guard conditions are then estimated based on the segmentation of traces. However, such a framework requires a good prior knowledge of the target system to select features for the clustering.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces

Yang

Beg

Kenigsberg

et al. 2022

ACM Trans. Cyber-Phys. Syst.

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Automata-based modeling of hybrid and cyber-physical systems (CPS) is an important formal abstraction amenable to algorithmic analysis of its dynamic behaviors, such as in verification, fault identification, and anomaly detection. However, for realistic systems, especially industrial ones, identifying hybrid automata is challenging, due in part to inferring hybrid interactions, which involves inference of both continuous behaviors, such as through classical system identification, as well as discrete behaviors, such as through automata (e.g., L*) learning. In this paper, we propose and evaluate a framework for inferring and validating models of deterministic hybrid systems with linear ordinary differential equations (ODEs) from input/output execution traces. The framework contains algorithms for the approximation of continuous dynamics in discrete modes, estimation of transition conditions, and the inference of automata mode merging. The algorithms are capable of clustering trace segments and estimating their dynamic parameters, and meanwhile, deriving guard conditions that are represented by multiple linear inequalities. Finally, the inferred model is automatically converted to the format of the original system for the validation. We demonstrate the utility of this framework by evaluating its performance in several case studies as implemented through a publicly available prototype software framework called HAutLearn and compare it with a membership-based algorithm.

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Section: Related Workmentioning

confidence: 99%

A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces

Yang

Beg

Kenigsberg

et al. 2022

ACM Trans. Cyber-Phys. Syst.

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“…Automata learning is a field of studies with a wellestablished and extensive research history [9], [10], [15], [18]. The closest articles to our work are those that model the behavior of hybrid systems using a hybrid automaton [1], [2], [14]. In [1], the authors proposed a passive offline framework for learning hybrid automata.…”

Section: B Ecu Of Anti-lock Brake Systemmentioning

confidence: 99%

“…For example, the authors in [9] extract DFA from recurrent neural networks (RNNs) and provide an interpretable model of RNNs. In [14], the authors exploit Fisher Information analysis and Cramer Rao bound theorem to construct a linear hybrid automaton and model the behavior of an Artificial Pancreas. We can categorize approaches for auotmata learning in two different ways [15]: 1) Active vs passive: Active learning algorithms directly interact with SUL, and hence, they could request any traces of inputs, along with their outputs.…”

Section: Introductionmentioning

confidence: 99%

A Passive Online Technique for Learning Hybrid Automata from Input/Output Traces

Saberi¹,

Faghih²,

Bavil³

2021

Preprint

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Specification synthesis is the process of deriving a model from the input-output traces of a system. It is used extensively in test design, reverse engineering, and system identification. One type of the resulting artifact of this process for cyber-physical systems is hybrid automata. They are intuitive, precise, tool independent, and at a high level of abstraction, and can model systems with both discrete and continuous variables. In this paper, we propose a new technique for synthesizing hybrid automaton from the input-output traces of a non-linear cyberphysical system. Similarity detection in non-linear behaviors is the main challenge for extracting such models. We address this problem by utilizing the Dynamic Time Warping technique. Our approach is passive, meaning that it does not need interaction with the system during automata synthesis from the logged traces; and online, which means that each input/output trace is used only once in the procedure. In other words, each new trace can be used to improve the already synthesized automaton. We evaluated our algorithm in two industrial and simulated case studies. The accuracy of the derived automata show promising results.

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“…We propose a safety verification approach based on automated mining of hybrid automata from input/output traces collected from the operation of AI-enabled cyber-physical systems (Lamrani, Banerjee, and Gupta 2018b). An AIenabled CPS is a system comprising a perception component, a planner/controller, and the environment (system under control) (Russell and Norvig 2016).…”

Section: Safety Verification Ha-miningmentioning

confidence: 99%

Toward Operational Safety Verification of AI-Enabled CPS (Student Abstract)

Lamrani

Banerjee

Gupta

2020

AAAI

Self Cite

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AI-enabled Cyber-physical systems (CPS) such as artificial pancreas (AP) or autonomous cars are using machine learning to make several critical decisions. The system is subject to inputs and scenarios which are not observed during training and the expected outputs are not known. Hence, popular model based verification techniques that characterize behavior of a control system before deployment using predictive models may be inaccurate and often result in incorrect safety analysis results. In addition, regulatory agencies are required to regulate safety-critical AI enabled CPS to ensure their operational safety. However, high complexity of the system result in myriad of safety concerns all of which may not only be comprehensively tested before deployment but also may not even be detected during design and testing phase. In this work, we propose a tool to help regulatory agencies compare the operation of the CPS with the specifications given by the manufacturer to ensure that the operation results conform with the safety assured design of a CPS.

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HyMn: Mining linear hybrid automata from input output traces of cyber-physical systems

Cited by 17 publications

References 3 publications

A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces

A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces

A Passive Online Technique for Learning Hybrid Automata from Input/Output Traces

Toward Operational Safety Verification of AI-Enabled CPS (Student Abstract)

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