Formal Methods Meet Machine Learning

• Published in: Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning Vol. 13703; pp. 393 - 405
• Main Authors: Larsen, Kim, Legay, Axel, Nolte, Gerrit, Schlüter, Maximilian, Stoelinga, Marielle, Steffen, Bernhard
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• ISBN: 3031197585; 9783031197581
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-19759-8_24

The field of machine learning focuses on computationally efficient, yet approximate algorithms. On the contrary, the field of formal methods focuses on mathematical rigor and provable correctness. Despite their superficial differences, both fields offer mutual benefit. Formal methods offer methods to verify and explain machine learning systems, aiding their adoption in safety critical domains. Machine learning offers approximate, computationally efficient approaches that let formal methods scale to larger problems. This paper gives an introduction to the track “Formal Methods Meets Machine Learning” (F3ML) and shortly presents its scientific contributions, structured into two thematic subthemes: One, concerning formal methods based approaches for the explanation and verification of machine learning systems, and one concerning the employment of machine learning approaches to scale formal methods.