Active Prediction for Discrete Event Systems

Authors Stefan Haar , Serge Haddad , Stefan Schwoon , Lina Ye



PDF
Thumbnail PDF

File

LIPIcs.FSTTCS.2020.48.pdf
  • Filesize: 0.51 MB
  • 16 pages

Document Identifiers

Author Details

Stefan Haar
  • INRIA, LSV, ENS Paris-Saclay, CNRS, Université Paris-Saclay, France
Serge Haddad
  • LSV, ENS Paris-Saclay, CNRS, INRIA, Université Paris-Saclay, France
Stefan Schwoon
  • LSV, ENS Paris-Saclay, CNRS, INRIA, Université Paris-Saclay, France
Lina Ye
  • LRI, Université Paris-Saclay, CentraleSupélec, France

Cite As Get BibTex

Stefan Haar, Serge Haddad, Stefan Schwoon, and Lina Ye. Active Prediction for Discrete Event Systems. In 40th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 182, pp. 48:1-48:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.FSTTCS.2020.48

Abstract

A central task in partially observed controllable system is to detect or prevent the occurrence of certain events called faults. Systems for which one can design a controller avoiding the faults are called actively safe. Otherwise, one may require that a fault is eventually detected, which is the task of diagnosis. Systems for which one can design a controller detecting the faults are called actively diagnosable. An intermediate requirement is prediction, which consists in determining that a fault will occur whatever the future behaviour of the system. When a system is not predictable, one may be interested in designing a controller to make it so. Here we study the latter problem, called active prediction, and its associated property, active predictability. In other words, we investigate how to determine whether or not a system enjoys the active predictability property, i.e., there exists an active predictor for the system. 
Our contributions are threefold. From a semantical point of view, we refine the notion of predictability by adding two quantitative requirements: the minimal and maximal delay before the occurence of the fault, and we characterize the requirements fulfilled by a controller that performs predictions. Then we show that active predictability is EXPTIME-complete where the upper bound is obtained via a game-based approach. Finally we establish that active predictability is equivalent to active safety when the maximal delay is beyond a threshold depending on the size of the system, and we show that this threshold is accurate by exhibiting a family of systems fulfilling active predictability but not active safety.

Subject Classification

ACM Subject Classification
  • Theory of computation → Formal languages and automata theory
  • Mathematics of computing → Discrete mathematics
Keywords
  • Automata Theory
  • Partially observed systems
  • Diagnosability
  • Predictability

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. N. Bertrand, E. Fabre, S. Haar, S. Haddad, and L. Hélouët. Active diagnosis for probabilistic systems. In FOSSACS 2014, Grenoble, France, volume 8412 of LNCS, pages 29-42, 2014. Google Scholar
  2. N. Bertrand, S. Haddad, and E. Lefaucheux. Foundation of Diagnosis and Predictability in Probabilistic Systems. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS'14), volume 29 of LIPIcs, pages 417-429, New Delhi, India, December 2014. Google Scholar
  3. D. Berwanger and L. Doyen. On the power of imperfect information. In Proc. FSTTCS, volume 2 of LIPICS, pages 73-82, Bangalore, India, 2008. Google Scholar
  4. S. Böhm, S. Haar, S. Haddad, P. Hofman, and S. Schwoon. Active diagnosis with observable quiescence. In Proc. CDC: 54th IEEE Conf. on Decision and Control, pages 1663-1668, Osaka, Japan, December 2015. Google Scholar
  5. L. Brandán Briones and A. Madalinski. Bounded predictability for faulty discrete event systems. In 30nd International Conference of the Chilean Computer Science Society, SCCC, pages 142-146, Curico, Chile, November 2011. Google Scholar
  6. C. G. Cassandras and S. Lafortune. Introduction to Discrete Event Systems - Second Edition. Springer, 2008. Google Scholar
  7. F. Cassez and S. Tripakis. Fault diagnosis with static and dynamic observers. Fundamenta Informaticae, 88:497-540, 2008. Google Scholar
  8. F. Cassez and S. Tripakis. Fault diagnosis with static and dynamic observers. Fundam. Informaticae, 88(4):497-540, 2008. Google Scholar
  9. E. Chanthery and Y. Pencolé. Monitoring and active diagnosis for discrete-event systems. In Proc. SafeProcess'09, pages 1545-1550, 2009. Google Scholar
  10. E. Dallal and S. Lafortune. On most permissive observers in dynamic sensor activation problems. IEEE Trans. Autom. Control., 59(4):966-981, 2014. Google Scholar
  11. S. Genc and S. Lafortune. Predictability of event occurrences in partially-observed discrete-event systems. Autom., 45(2):301-311, 2009. URL: https://doi.org/10.1016/j.automatica.2008.06.022.
  12. E. Grädel, W. Thomas, and T. Wilke, editors. Automata, Logics, and Infinite Games: A Guide to Current Research, volume 2500 of Lecture Notes in Computer Science. Springer, 2002. Google Scholar
  13. S. Haar, S. Haddad, T. Melliti, and S. Schwoon. Optimal constructions for active diagnosis. Journal of Computer and System Sciences, 83(1):101-120, 2017. Google Scholar
  14. Stefan Haar, Serge Haddad, Stefan Schwoon, and Lina Ye. Active Prediction for Discrete Event Systems. working paper or preprint, September 2020. URL: https://hal.archives-ouvertes.fr/hal-02951944.
  15. A. Madalinski and V. Khomenko. Predictability verification with parallel LTL-X model checking based on Petri net unfoldings. IFAC Proceedings Volumes, 45(20):1232-1237, 2012. 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. Google Scholar
  16. M. Sampath, S. Lafortune, and D. Teneketzis. Active diagnosis of discrete-event systems. IEEE Transactions on Automatic Control, 43(7):908-929, July 1998. Google Scholar
  17. M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. Teneketzis. Diagnosability of discrete-event systems. IEEE Trans. Aut. Cont., 40(9):1555-1575, 1995. Google Scholar
  18. L. Ye, P. Dague, and F. Nouioua. Predictability Analysis of Distributed Discrete Event Systems. In 52nd IEEE Conference on Decision and Control, pages 5009-5015, Florence, Italy, December 2013. Google Scholar
  19. X. Yin and S. Lafortune. A uniform approach for synthesizing property-enforcing supervisors for partially-observed discrete-event systems. IEEE Trans. Autom. Control., 61(8):2140-2154, 2016. Google Scholar
  20. X. Yin and S. Lafortune. A general approach for optimizing dynamic sensor activation for discrete event systems. Autom., 105:376-383, 2019. Google Scholar
  21. X. Yin and Z. Li. Decentralized fault prognosis of discrete event systems with guaranteed performance bound. Autom., 69:375-379, 2016. Google Scholar
  22. T-S. Yoo and S. Lafortune. Polynomial-time verification of diagnosability of partially observed discrete-event systems. IEEE Trans. Automat. Contr., 47(9):1491-1495, 2002. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail