Skip to main content
Springer Nature Link
Log in

No Explanation Without (Fuzzy) Representation

  • Conference paper
  • First Online:
Advances in Computational Intelligence Systems (UKCI 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1454))

Included in the following conference series:

  • 184 Accesses

Abstract

Explainability is seen as a necessary step in the adoption of AI, and is a building block for collaborative AI systems which combine the strengths of machines and humans in tackling problems such as the rapid analysis of cyber-security data. In this paper we argue that symbolic representations are a necessary component in the interface between humans and AI components for both explanation and the wider goal of collaborative systems. We show that fuzzy conceptual graphs are a feasible representation of general and specific knowledge in the domain of cyber-security, and illustrate that reasoning can enable the automatic generation of new knowledge.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html.

  2. 2.

    See www.lirmm.fr/cogui for example.

  3. 3.

    docs.oasis-open.org/cti/stix/v2.1/stix-v2.1.pdf.

References

  1. Gunning, D., Aha, D.: DARPA’s explainable artificial intelligence (XAI) program. AI Mag. 40(2), 44–58 (2019)

    Google Scholar 

  2. DARPA. Explainable Artificial Intelligence (XAI) (2016). www.darpa.mil/attachments/DARPA-BAA-16-53.pdf. Accessed Dec 2016

  3. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)

    Article  Google Scholar 

  4. Gilpin, L., et al.: Explaining explanations: an approach to evaluating interpretability of machine learning. In: 5th IEEE International Conference on Data Science and Advanced Analytics (2018). arXiv:1806.00069

    Google Scholar 

  5. Carvalho, D.V., Pereira, E.M., Cardoso, J.S.: Machine learning interpretability: a survey on methods and metrics. Electronics 8, 832 (2019)

    Article  Google Scholar 

  6. Martin, T.P., Azvine, B.: Graded concepts for collaborative intelligence. In: Proceedings of 2018 IEEE International Conference Systems, Man, and Cybernetics, SMC 2018, pp. 2589–2594 (2018)

    Google Scholar 

  7. Doran, D., Schulz, S., Besold, T.R.: What does explainable AI really mean? A new conceptualization of perspectives. In: CEUR Workshop Proceedings, vol. 2071 (2018)

    Google Scholar 

  8. Lipton, Z.C.: The Mythos of Model Interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16, 31–57 (2018)

    Article  Google Scholar 

  9. Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019)

    Article  MathSciNet  Google Scholar 

  10. Moore, J., Swartout, W.: Explanation in expert systems: a survey (1989)

    Google Scholar 

  11. Kidd, A.L., Cooper, M.B.: Man-machine interface issues in the construction and use of an expert system. Int. J. Man Mach. Stud. 22, 91–102 (1985)

    Article  Google Scholar 

  12. Teach, R.L., Shortliffe, E.H.: An analysis of physician attitudes regarding computer-based clinical consultation systems. Comp. Biomed. Res 14, 542–558 (1981)

    Article  Google Scholar 

  13. Teach, R.L., Shortliffe, E.H.: An analysis of physician attitudes regarding computer-based clinical consultation systems. In: Anderson, J.G., Jay, S.J. (eds.) Use and Impact of Computers in Clinical Medicine, pp. 68–85. Springer, New York (1987). https://doi.org/10.1007/978-1-4613-8674-2_6

    Chapter  Google Scholar 

  14. Tiddi, I., Schlobach, S.: Knowledge graphs as tools for explainable machine learning: a survey. Artif. Intell. 302, 103627 (2022)

    Article  MathSciNet  Google Scholar 

  15. Sowa, J.F.: Conceptual Structures. Addison Wesley, Boston (1984)

    Google Scholar 

  16. Chein, M., Mugnier, M.L.: Graph-based knowledge representation - computational foundations of conceptual graphs. In: Advanced Info and Knowledge Processing (2009)

    Google Scholar 

  17. Faci, A., Lesot, M.J., Laudy, C.: Fuzzy conceptual graphs: a comparative discussion. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2021)

    Google Scholar 

  18. Martin, T.P.: The X-mu representation of fuzzy sets. Soft. Comput. 19, 1497–1509 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Lab, University of Bristol, Bristol, BS8 1UB, UK

    Trevor Martin

Authors
  1. Trevor Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Trevor Martin .

Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK

    George Panoutsos

  2. Department of Automatic Control and Systems Engineering, Head of the Intelligent Systems Research Laboratory, The University of Sheffield, Sheffield, South Yorkshire, UK

    Mahdi Mahfouf

  3. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, South Yorkshire, UK

    Lyudmila S Mihaylova

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martin, T. (2024). No Explanation Without (Fuzzy) Representation. In: Panoutsos, G., Mahfouf, M., Mihaylova, L.S. (eds) Advances in Computational Intelligence Systems. UKCI 2022. Advances in Intelligent Systems and Computing, vol 1454. Springer, Cham. https://doi.org/10.1007/978-3-031-55568-8_3

Download citation

Publish with us

Policies and ethics