skip to main content
10.1145/3277593.3277629acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiotConference Proceedingsconference-collections
short-paper

Semantic parsing of automobile steering systems

Published: 15 October 2018 Publication History

Abstract

Formal specification plays crucial roles in the rigorous verification and design of automobile steering systems. The challenge of getting high-quality formal specifications is well documented. This paper presents a problem called 'semantic parsing', the goal of which is to automatically translate the behavior of an automobile steering system to a formal specification written in signal temporal logic (STL) with human-in-the loop manner. To tackle the combinatorial explosion inherent to the problem, this paper adopts a search strategy called agenda-based parsing, which is inspired by natural language processing. Based on such a strategy, the semantic parsing problem can be formulated as a Markov decision process (MDP) and then solved using reinforcement learning. The obtained formal specification can be viewed as an interpretable classifier, which, on the one hand, can classify desirable and undesirable behaviors, and, on the other hand, is expressed in a human-understandable form. The performance of the proposed method is demonstrated with study.

References

[1]
Ali, N., Guéhéneuc, Y.-G., and Antoniol, G. Trustrace: Mining software repositories to improve the accuracy of requirement traceability links. IEEE Transactions on Software Engineering 39, 5 (2013), 725--741.
[2]
Berant, J., and Liang, P. Imitation learning of agenda-based semantic parsers. Transactions of the Association for Computational Linguistics 3 (2015), 545--558.
[3]
Chen, G., Sabato, Z., and Kong, Z. Formal interpretation of cyber-physical system performance with temporal logic. Cyber-Physical Systems (2018)
[4]
Donzé, A., and Maler, O. Robust satisfaction of temporal logic over real-valued signals. In International Conference on Formal Modeling and Analysis of Timed Systems, Springer (2010), 92--106.
[5]
Kong, Z., Jones, A., and Belta, C. Temporal logics for learning and detection of anomalous behavior. IEEE Transactions on Automatic Control 62, 3 (2017), 1210--1222.
[6]
Seshia, S. A., Sadigh, D., and Sastry, S. S. Towards verified artificial intelligence. arXiv preprint arXiv.1606.08514 (2016).
[7]
Van Lamsweerde, A. Requirements engineering: From system goals to UML models to software, vol. 10. Chichester, UK: John Wiley & Sons, 2009.

Cited By

View all
  • (2024)The Augmented Intelligence Perspective on Human-in-the-Loop Reinforcement Learning: Review, Concept Designs, and Future DirectionsIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.346737054:6(762-777)Online publication date: Dec-2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
IOT '18: Proceedings of the 8th International Conference on the Internet of Things
October 2018
299 pages
ISBN:9781450365642
DOI:10.1145/3277593
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. formal specification
  2. reinforcement learning
  3. semantic parsing
  4. signal temporal logic
  5. steering systems

Qualifiers

  • Short-paper

Conference

IOT '18
IOT '18: 8th International Conference on the Internet of Things
October 15 - 18, 2018
California, Santa Barbara, USA

Acceptance Rates

Overall Acceptance Rate 28 of 84 submissions, 33%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)The Augmented Intelligence Perspective on Human-in-the-Loop Reinforcement Learning: Review, Concept Designs, and Future DirectionsIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.346737054:6(762-777)Online publication date: Dec-2024

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media