Skip to main content

A Self-certifiable Architecture for Critical Systems Powered by Probabilistic Logic Artificial Intelligence

  • Conference paper
  • First Online:
Computer Safety, Reliability, and Security (SAFECOMP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11699))

Included in the following conference series:

  • 2629 Accesses

Abstract

We present a versatile architecture for AI-powered self-adaptive self-certifiable critical systems. It aims at supporting semi-automated low-cost re-certification for self-adaptive systems after each adaptation of their behavior to a persistent change in their operational environment throughout their lifecycle.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Since lack of space prevents us to insert all the relevant references in the bibliography of this short paper, see https://dtai.cs.kuleuven.be/CHR/biblio.shtml for a more complete one.

References

  1. Boulanger, J.: Safety Management for Software-Based Equipment. Wiley, Hoboken (2013)

    Book  Google Scholar 

  2. Lalanda, P., McCann, J., Diaconescu, A.: Autonomic Computing: Principles, Design and Implementation. Springer, London (2013). https://doi.org/10.1007/978-1-4471-5007-7

    Book  Google Scholar 

  3. Frühwirth, T.: Constraint Handling Rules. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  4. Riguzzi, F.: Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning, Rivers Publishers 2018

    Google Scholar 

  5. Sneyers, J., Wannes, M., Vennekens, J.: CHRiSM: chance rules induce statistical models. In: Proceedings of the 6th International Workshop on Constraint Handling Rules, Pasadena, CA, USA (2009)

    Google Scholar 

  6. Sneyers, J., De Schreye, D., Frühwirth, T.: Probabilistic legal reasoning in CHRiSM. Theor. Pract. Log. Prog. 13(4–5), 769–781 (2013)

    Article  MathSciNet  Google Scholar 

  7. Sneyers, J., Meert, W., Vennekens, J., Kameya, Y., Sato, T.: CHR(PRISM)-based probabilistic logic learning. Theor. Pract. Log. Prog. 10, 433–447 (2010)

    Article  MathSciNet  Google Scholar 

  8. Muñoz, J., Tamura, G., Raicu, I., Mazo, R., Salinesi, C.: REFAS: a PLE approach for simulation of self-adaptive systems requirements. In: Proceedings of the 19th International Software Product Line Conference (SPLC 2015), Nashville, TN, USA (2015)

    Google Scholar 

  9. Almeida da Silva, M., Mougenot, A., Blanc, X., Bendraou, R.: Towards automated inconsistency handling in design models. In: 22nd International Conference on Advanced Information Systems Engineering, Hammamet, Tunisia (2010)

    Google Scholar 

  10. Manhave, R., Dumancic, S., Kimmig, A., Demeester, T., De Raedt, L.: DeepProbLog: deep neural probabilistic programming. In: Proceedings of the 32nd Conference on Neural Information Processing (NeurIPS), Montreal, Canada (2018)

    Google Scholar 

  11. Triossi, A., Orlando, S., Raffaetá, A., Frühwirth, T.: Compiling CHR to parallel hardware. In: Proceedings of the 14th International ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming, Leuven, Belgium (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jacques Robin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Robin, J., Mazo, R., Madeira, H., Barbosa, R., Diaz, D., Abreu, S. (2019). A Self-certifiable Architecture for Critical Systems Powered by Probabilistic Logic Artificial Intelligence. In: Romanovsky, A., Troubitsyna, E., Gashi, I., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2019. Lecture Notes in Computer Science(), vol 11699. Springer, Cham. https://doi.org/10.1007/978-3-030-26250-1_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26250-1_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26249-5

  • Online ISBN: 978-3-030-26250-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics