Event-B Refinement for Continuous Behaviours Approximation

Dupont, Guillaume; Aït‐Ameur, Yamine; Pantel, Marc; Singh, Neeraj Kumar

doi:10.1007/978-3-030-88885-5_21

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Dupont et al [8] define approximate notions of refinement for continuous systems. Their work considers two different views of conformance: upwards approximation, where an approximated system is refined to an exact system, and downwards approximation, the inverse.…”

Section: Discussionmentioning

confidence: 99%

Modelling and Verifying Robotic Software that Uses Neural Networks

Attala,

Cavalcanti,

Woodcock

2023

Lecture Notes in Computer Science

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Verifying learning robotic systems is challenging. Existing techniques and tools for verification of an artificial neural network (ANN) are concerned with component-level properties. Here, we deal with robotic systems whose control software uses ANN components, and with properties of that software that may depend on all components. Our focus is on trained fully connected ReLU neural networks for control. We present an approach to (1) modelling ANN components as part of behavioural models for control software and (2) verification using traditional and ANN-specific verification tools. We describe our results in the context of RoboChart, a domain-specific modelling language for robotics with support for formal verification. We describe our modelling notation and a strategy for automated proof using Isabelle and Marabou, for example.

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Section: Discussionmentioning

confidence: 99%

Modelling and Verifying Robotic Software that Uses Neural Networks

Attala,

Cavalcanti,

Woodcock

2023

Lecture Notes in Computer Science

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show abstract

“…The Theory extension provides a way to add new data types, operators, inference and rewrite rules, as well as code generation translation rules (Butler and Maamria, 2013). There is also an extension of the theory of Real Numbers for embracing discrete and continuous functionalities of hybrid systems in Event-B where all the relevant definitions, theorems and proof rules related to continuous functions are defined (Dupont et al, 2018).…”

Section: Beginner Supportmentioning

confidence: 99%

Applying guidelines for system modelling in Event-B - a systematic literature review

Olszewska

Manninen

Edmunds

et al. 2020

IJCCBS

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Developing safety-critical systems is an intricate task since it involves the application of well-established and rigorous methods, supported by good practices. The modelling is merely a part of this undertaking. However, it plays a significant role in the description of the system, how it will behave and what properties it will have. Formal methods, for instance Event-B, are utilised in such cases to assure that the system is correct-by-construction and functions as required. In this work we use a literature review method to collect a body of knowledge that would support the Event-B practitioners with modelling guidelines. We first define the domains in which the guidelines fall and divide them into two categories: beginners and advanced. Then we provide a collection of guidelines as reinforcements for domain-specific applications of Event-B.

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Event-B Refinement for Continuous Behaviours Approximation

Cited by 2 publications

References 14 publications

Modelling and Verifying Robotic Software that Uses Neural Networks

Modelling and Verifying Robotic Software that Uses Neural Networks

Applying guidelines for system modelling in Event-B - a systematic literature review

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