Druck auf Anfrage Neuware - Printed after ordering - 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 methodstechniques to discussing on how formal methods can be used to increase predictability, explainability, and accountability of ML-enabledautonomous systems. NSV 2022 is focusing on the challenges of the verification of cyber-physical systems with machine learning components.

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Bibliographic Details

Title: 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
Publisher: Springer International Publishing
Publication Date: 2022
Language: English
ISBN 10: 3031212215
ISBN 13: 9783031212215
Binding: Taschenbuch
Condition: Neu
Dimensions: 235 millimeters width by 155 millimeters height by 12 millimeters depth
Weight: 335 grams

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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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