Attala, Ziggy, Cavalcanti, Ana orcid.org/0000-0002-0831-1976 and Woodcock, Jim orcid.org/0000-0001-7955-2702 (2023) Modelling and Verifying Robotic Software that Uses Neural Networks. In: Ábrahám, Erika, Dubslaff, Clemens and Tarifa, Silvia Lizeth, (eds.) Theoretical Aspects of Computing – ICTAC 2023 - 20th International Colloquium, Proceedings. 20th International Colloquium on Theoretical Aspects of Computing, ICTAC 2023, 04-08 Dec 2023 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer Science and Business Media Deutschland GmbH , PER , pp. 15-35.
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Funding Information: This work has been funded by the UK EPSRC Grants EP/R025479/1, and EP/V026801/2, and by the UK Royal Academy of Engineering Grant No CiET1718/45. Publisher Copyright: © 2023 The Author(s). This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | CSP,Isabelle,Marabou,theorem proving,verification |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 15 Dec 2023 14:10 |
Last Modified: | 07 Feb 2025 00:09 |
Published Version: | https://doi.org/10.1007/978-3-031-47963-2_3 |
Status: | Published |
Publisher: | Springer Science and Business Media Deutschland GmbH |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-031-47963-2_3 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206676 |
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