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Achieving Complete Coverage with Hypercube-Based Symbolic Knowledge-Extraction Techniques

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Artificial Intelligence. ECAI 2023 International Workshops (ECAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1947))

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Abstract

Symbolic knowledge-extraction (SKE) techniques are currently employed for various purposes, particularly addressing the challenge of explaining opaque models by generating human-understandable explanations. The existing literature encompasses a diverse range of techniques, each relying on specific theoretical assumptions and possessing its own advantages and disadvantages. Amongst the available choices, hypercube-based SKE techniques are notable for their adaptability and versatility. However, they may suffer from limited completeness when utilised for making predictions. This research aims to augment the predictive capabilities of hypercube-based SKE techniques, striving for a completeness rate of 100%. Furthermore, the study includes experiments that assess the effectiveness of the proposed enhancements.

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Notes

  1. 1.

    We use the term “hypercube” also for referring to actual hyperrectangles, as commonly made in the literature [9, for instance].

  2. 2.

    https://github.com/psykei/psyke-python.

  3. 3.

    https://archive.ics.uci.edu/dataset/53/iris.

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Acknowledgments

This work has been supported by European Union’s Horizon Europe AEQUITAS research and innovation programme under grant number 101070363.

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Authors and Affiliations

  1. Department of Pure and Applied Sciences (DiSPeA), University of Urbino “Carlo Bo”, Urbino, Italy

    Federico Sabbatini

  2. Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy

    Roberta Calegari

Authors
  1. Federico Sabbatini
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  2. Roberta Calegari
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Corresponding author

Correspondence to Federico Sabbatini .

Editor information

Editors and Affiliations

  1. Halmstad University, Halmstad, Sweden

    Sławomir Nowaczyk

  2. Warsaw University of Technology, Warsaw, Poland

    Przemysław Biecek

  3. Warsaw University, Warsaw, Poland

    Neo Christopher Chung

  4. University of Huddersfield, Huddersfield, UK

    Mauro Vallati

  5. AGH University of Science and Technology, Kraków, Poland

    Paweł Skruch

  6. AGH University of Science and Technology, Kraków, Poland

    Joanna Jaworek-Korjakowska

  7. University of Huddersfield, Huddersfield, UK

    Simon Parkinson

  8. University of Huddersfield, Huddersfield, UK

    Alexandros Nikitas

  9. Universität Osnabrück, Osnabrück, Germany

    Martin Atzmüller

  10. University of Economics Prague, Prague, Czech Republic

    Tomáš Kliegr

  11. University of Bamberg, Bamberg, Germany

    Ute Schmid

  12. Jagiellonian University, Kraków, Poland

    Szymon Bobek

  13. Jožef Stefan Institute, Ljubljana, Slovenia

    Nada Lavrac

  14. HU University of Applied Sciences Utrecht, Utrecht, The Netherlands

    Marieke Peeters

  15. Rotterdam University of Applied Sciences, Rotterdam, The Netherlands

    Roland van Dierendonck

  16. Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Saskia Robben

  17. University of Reims Champagne-Ardenne, Reims, France

    Eunika Mercier-Laurent

  18. Istanbul Technical University, Istanbul, Türkiye

    Gülgün Kayakutlu

  19. Wroclaw University of Economics and Business, Wrocław, Poland

    Mieczyslaw Lech Owoc

  20. University of Galway, Galway, Ireland

    Karl Mason

  21. University of Galway, Galway, Ireland

    Abdul Wahid

  22. University of Calabria, Rende, Italy

    Pierangela Bruno

  23. University of Calabria, Rende, Italy

    Francesco Calimeri

  24. Marche Polytechnic University, Ancona, Italy

    Francesco Cauteruccio

  25. University of Calabria, Rende, Italy

    Giorgio Terracina

  26. University of Bamberg, Bamberg, Germany

    Diedrich Wolter

  27. Coburg University of Applied Sciences, Coburg, Germany

    Jochen L. Leidner

  28. FAU Erlangen-Nürnberg, Erlangen, Germany

    Michael Kohlhase

  29. University of Leeds, Leeds, UK

    Vania Dimitrova

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Sabbatini, F., Calegari, R. (2024). Achieving Complete Coverage with Hypercube-Based Symbolic Knowledge-Extraction Techniques. In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol 1947. Springer, Cham. https://doi.org/10.1007/978-3-031-50396-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-50396-2_10

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