Time to Learn – Learning Timed Automata from Tests

Tappler, Martin; Aichernig, Bernhard K.; Larsen, Kim Guldstrand; Lorber, Florian

doi:10.1007/978-3-030-29662-9_13

Cited by 29 publications

(12 citation statements)

References 44 publications

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“…Signiicant related works have been developed in the context of automata learning for purely discrete systems, such as inite state automata [4]. For timed, switched, and hybrid systems, there has been less investigation, although there are several recently proposed methods [36,44,49,51]. From the control theory, there are more related works for the system identiication, including the identiication of piecewise models such as the Switched aine AutoRegressive eXogenous (SARX) model and the PieceWise aine ARX (PWARX) model.…”

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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“…Medhat et al [14] use a modification of Angluin's 𝐿 * algorithm [5] to learn the discrete structure separately from the dynamics. Tappler et al [20] on the other hand learn a timed automaton with genetic programming. Note that these automata alleviate the need to infer dynamics.…”

Section: Comparison Against Other Approachesmentioning

confidence: 99%

“…Genetic Programming. Tappler et al [20] use genetic programming to successively adapt a candidate automaton to encompass all input traces. As an example, they consider a timed automaton modeling a car alarm system (see Figure 2b).…”

Section: Comparison Against Other Approachesmentioning

confidence: 99%

Conservative Hybrid Automata from Development Artifacts

Niklas¹,

Schmitt²,

Schwenger³

2021

Preprint

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The verification of cyber-physical systems operating in a safetycritical environment requires formal system models. The validity of the verification hinges on the precision of the model: possible behavior not captured in the model can result in formally verified, but unsafe systems. Yet, manual construction is delicate and errorprone while automatic construction does not scale for large and complex systems. As a remedy, this paper devises an automatic construction algorithm that utilizes information contained in artifacts of the development process: a runtime monitoring specification and recorded test traces. These artifacts incur no additional cost and provide sufficient information so that the construction process scales well for large systems. The algorithm uses a hybrid approach between a top-down and a bottom-up construction which allows for proving the result conservative, while limiting the level of over-approximation. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems; • Theory of computation → Timed and hybrid models; Formal languages and automata theory.

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“…In addition, the passive learning methods cited above concern only discrete-time semantics of the underlying timed models, i.e., the clock takes values from non-negative integers. We furthermore refer the readers to [11,29] for learning specialized forms of practical timed systems in a passive manner, [34] for passively learning timed automata using genetic programming which scales to automata of the large size, [30] for learning probabilistic real-time automata incorporating clustering techniques in machine learning, and [33] for L * -based learning of Markov decision processes with testing and sampling.…”

Section: Introductionmentioning

confidence: 99%

Learning One-Clock Timed Automata

Chen

Zhan

et al. 2020

Lecture Notes in Computer Science

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We present an algorithm for active learning of deterministic timed automata with a single clock. The algorithm is within the framework of Angluin's L * algorithm and inspired by existing work on the active learning of symbolic automata. Due to the need of guessing for each transition whether it resets the clock, the algorithm is of exponential complexity in the size of the learned automata. Before presenting this algorithm, we propose a simpler version where the teacher is assumed to be smart in the sense of being able to provide the reset information. We show that this simpler setting yields a polynomial complexity of the learning process. Both of the algorithms are implemented and evaluated on a collection of randomly generated examples. We furthermore demonstrate the simpler algorithm on the functional specification of the TCP protocol.

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Time to Learn – Learning Timed Automata from Tests

Cited by 29 publications

References 44 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

Conservative Hybrid Automata from Development Artifacts

Learning One-Clock Timed Automata

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