Software Verification and Formal Methods for ML-Enabled Autonomous Systems | 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022, Proceedings. Questo articolo non è disponibile.
Lingua: inglese
Editore: Springer, 2022
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Software Verification and Formal Methods for ML-Enabled Autonomous Systems | 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022, Proceedings | Omri Isac (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2022 | Springer | EAN 9783031212215 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Codice articolo 125728272
- Titolo
- Software Verification and Formal Methods for ML-Enabled Autonomous Systems | 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, Israel, July 31 - August 1, and August 11, 2022, Proceedings
- Autore
- Omri Isac (u. a.)
- Editore
- Springer
- Anno di pubblicazione
- 2022
- Condizione
- Neu
- Rilegatura
- Taschenbuch
- Lingua
- inglese
- ISBN 10
- 3031212215
- ISBN 13
- 9783031212215
- Peso dell'articolo
- 335 grammi
- Dimensioni
- 235 x 155 x 12 mm
- Cataloghi dei venditori
- Bücher
This book constitutes the refereed proceedings of the 5th International Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022, and the 15th International Workshop on Numerical Software Verification, NSV 2022, which took place in Haifa, Israel, in July/August 2022.
The volume contains 8 full papers from the FoMLAS 2022 workshop and 3 full papers from the NSV 2022 workshop. The FoMLAS workshop is dedicated to the development of novel formal methods techniques to discussing on how formal methods can be used to increase predictability, explainability, and accountability of ML-enabled autonomous systems. NSV 2022 is focusing on the challenges of the verification of cyber-physical systems with machine learning components.
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