Formal Specification for Learning-Enabled Autonomous Systems

Saddek Bensalem; Chih Hong Cheng; Xiaowei Huang; Panagiotis Katsaros; Adam Molin; Dejan Nickovic; Doron Peled

doi:10.1007/978-3-031-21222-2_8

Formal Specification for Learning-Enabled Autonomous Systems

Saddek Bensalem
, Chih Hong Cheng
, Xiaowei Huang
, Panagiotis Katsaros
, Adam Molin
, Dejan Nickovic
, Doron Peled

Department of Computer Science

Université Grenoble Alpes
Fraunhofer Institute for Embedded Systems and Communication Technologies
University of Liverpool
Aristotle University of Thessaloniki
DENSO AUTOMOTIVE Deutschland GmbH
Austrian Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

The formal specification provides a uniquely readable description of various aspects of a system, including its temporal behavior. This facilitates testing and sometimes automatic verification of the system against the given specification. We present a logic-based formalism for specifying learning-enabled autonomous systems, which involve components based on neural networks. The formalism is based on first-order past time temporal logic that uses predicates for denoting events. We have applied the formalism successfully to two complex use cases.

Original language	English
Title of host publication	Software Verification and Formal Methods for ML-Enabled Autonomous Systems - 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Proceedings
Editors	Omri Isac, Guy Katz, Radoslav Ivanov, Nina Narodytska, Laura Nenzi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	131-143
Number of pages	13
ISBN (Print)	9783031212215
DOIs	https://doi.org/10.1007/978-3-031-21222-2_8
State	Published - 2022
Event	5th International Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022 and 15th International Workshop on Numerical Software Verification, NSV 2022 - Haifa, Israel Duration: 11 Aug 2022 → 11 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13466 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022 and 15th International Workshop on Numerical Software Verification, NSV 2022
Country/Territory	Israel
City	Haifa
Period	11/08/22 → 11/08/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Funding

Supported by the european project Horizon 2020 research and innovation programme under grant agreement No. 956123. C.-H. Cheng—The work is primarily conducted during his service at DENSO.

Funders	Funder number
Horizon 2020	956123

Keywords

First-order LTL
Formal specification
Learning-enabled systems
Neural networks

Access to Document

10.1007/978-3-031-21222-2_8

Cite this

Bensalem, S., Cheng, C. H., Huang, X., Katsaros, P., Molin, A., Nickovic, D., & Peled, D. (2022). Formal Specification for Learning-Enabled Autonomous Systems. In O. Isac, G. Katz, R. Ivanov, N. Narodytska, & L. Nenzi (Eds.), Software Verification and Formal Methods for ML-Enabled Autonomous Systems - 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Proceedings (pp. 131-143). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13466 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21222-2_8

Bensalem, Saddek ; Cheng, Chih Hong ; Huang, Xiaowei et al. / Formal Specification for Learning-Enabled Autonomous Systems. Software Verification and Formal Methods for ML-Enabled Autonomous Systems - 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Proceedings. editor / Omri Isac ; Guy Katz ; Radoslav Ivanov ; Nina Narodytska ; Laura Nenzi. Springer Science and Business Media Deutschland GmbH, 2022. pp. 131-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Bensalem, S, Cheng, CH, Huang, X, Katsaros, P, Molin, A, Nickovic, D & Peled, D 2022, Formal Specification for Learning-Enabled Autonomous Systems. in O Isac, G Katz, R Ivanov, N Narodytska & L Nenzi (eds), Software Verification and Formal Methods for ML-Enabled Autonomous Systems - 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13466 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 131-143, 5th International Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022 and 15th International Workshop on Numerical Software Verification, NSV 2022, Haifa, Israel, 11/08/22. https://doi.org/10.1007/978-3-031-21222-2_8

Formal Specification for Learning-Enabled Autonomous Systems. / Bensalem, Saddek; Cheng, Chih Hong; Huang, Xiaowei et al.
Software Verification and Formal Methods for ML-Enabled Autonomous Systems - 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Proceedings. ed. / Omri Isac; Guy Katz; Radoslav Ivanov; Nina Narodytska; Laura Nenzi. Springer Science and Business Media Deutschland GmbH, 2022. p. 131-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13466 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Bensalem S, Cheng CH, Huang X, Katsaros P, Molin A, Nickovic D et al. Formal Specification for Learning-Enabled Autonomous Systems. In Isac O, Katz G, Ivanov R, Narodytska N, Nenzi L, editors, Software Verification and Formal Methods for ML-Enabled Autonomous Systems - 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 131-143. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-21222-2_8