Software Verification and Formal Methods for ML-Enabled Autonomous Systems (Lecture Notes in Computer Science)


Software Verification and Formal Methods for ML-Enabled Autonomous Systems (Lecture Notes in Computer Science)
by: Omri Isac (Editor),Radoslav Ivanov (Editor),Guy Katz (Editor),Nina Narodytska (Editor),Laura Nenzi (Editor)&1more
Publisher: Springer
Edition: 1st ed. 2022
Publication Date: 2022/12/16
Language: English
Print Length: 216 pages
ISBN-10: 3031212215
ISBN-13: 9783031212215
Book Description
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.
About the Author
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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