Similarity Measures Between Arguments Revisited | SpringerLink
Skip to main content

Similarity Measures Between Arguments Revisited

  • Conference paper
  • First Online:
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11726))

Abstract

Recently, the notion of similarity between arguments, namely those built using propositional logic, has been investigated and several similarity measures have been defined. This paper shows that those measures may lead to inaccurate results when arguments are not concise, i.e., their supports contain information that is useless for inferring their conclusions. For circumventing this limitation, we start by refining arguments for making them concise. Then, we propose two families of similarity measures that extend existing ones and that deal with concise arguments.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

Notes

  1. 1.

    The letter \(\mathtt {A}\) in \(\mathtt {A}\)-\(\mathtt {CR}\) stands for “average”.

  2. 2.

    In this section, we slightly relax the notation by simply assuming that \(p\in \overline{\mathcal L}\). We will make similar assumptions throughout this section.

  3. 3.

    \(\mathtt {U}\) in \(\mathtt {U}\)-\(\mathtt {CR}\) stands for “union”.

References

  1. Amgoud, L., Prade, H.: Using arguments for making and explaining decisions. Artif. Intell. 173, 413–436 (2009)

    Article  MathSciNet  Google Scholar 

  2. Zhong, Q., Fan, X., Luo, X., Toni, F.: An explainable multi-attribute decision model based on argumentation. Expert Syst. Appl. 117, 42–61 (2019)

    Article  Google Scholar 

  3. García, A., Simari, G.: Defeasible logic programming: an argumentative approach. Theor. Pract. Logic Prog. 4(1–2), 95–138 (2004)

    Article  MathSciNet  Google Scholar 

  4. Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. J. Appl. Non-Class. Logics 7(1), 25–75 (1997)

    Article  MathSciNet  Google Scholar 

  5. Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artif. Intell. 128(1–2), 203–235 (2001)

    Article  MathSciNet  Google Scholar 

  6. Vesic, S.: Identifying the class of maxi-consistent operators in argumentation. J. Artif. Intell. Res. 47, 71–93 (2013)

    Article  MathSciNet  Google Scholar 

  7. Amgoud, L., Ben-Naim, J.: Axiomatic foundations of acceptability semantics. In: Proceedings of the Fifteenth International Conference on Principles of Knowledge Representation and Reasoning KR, pp. 2–11 (2016)

    Google Scholar 

  8. Amgoud, L., David, V.: Measuring similarity between logical arguments. In: Proceedings of the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning KR, pp. 98–107 (2018)

    Google Scholar 

  9. Amgoud, L., Bonzon, E., Delobelle, J., Doder, D., Konieczny, S., Maudet, N.: Gradual semantics accounting for similarity between arguments. In: Proceedings of the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning KR, pp. 88–97 (2018)

    Google Scholar 

  10. Lang, J., Liberatore, P., Marquis, P.: Propositional independence-formula-variable independence and forgetting. J. Artif. Intell. Res. 18, 391–443 (2003)

    Article  MathSciNet  Google Scholar 

  11. Amgoud, L., Besnard, P., Vesic, S.: Equivalence in logic-based argumentation. J. Appl. Non-Class. Logics 24(3), 181–208 (2014)

    Article  MathSciNet  Google Scholar 

  12. Jaccard, P.: Nouvelles recherches sur la distributions florale. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 223–270 (1901)

    Google Scholar 

  13. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  14. Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biol. Skr. 5, 1–34 (1948)

    Google Scholar 

  15. Anderberg, M.R.: Cluster Analysis for Applications. Probability and Mathematical Statistics: A Series of Monographs and Textbooks. Academic Press Inc., New York (1973)

    Google Scholar 

  16. Sneath, P.H., Sokal, R.R., et al.: Numerical taxonomy. In: The Principles and Practice of Numerical Classification (1973)

    Google Scholar 

  17. Ochiai, A.: Zoogeographical studies on the soleoid fishes found in Japan and its neighbouring regions. Bull. Jpn. Soc. Sci. Fischeries 22, 526–530 (1957)

    Article  Google Scholar 

  18. Kulczynski, S.: Die pflanzenassoziationen der pieninen. Bulletin International de l’Académie Polonaise des Sciences et des Lettres, Classe des Sciences Mathématiques et Naturelles, Série B, pp. 57–203 (1927)

    Google Scholar 

Download references

Acknowledgment

Support from the ANR-3IA Artificial and Natural Intelligence Toulouse Institute is gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Leila Amgoud , Victor David or Dragan Doder .

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

Amgoud, L., David, V., Doder, D. (2019). Similarity Measures Between Arguments Revisited. In: Kern-Isberner, G., Ognjanović, Z. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2019. Lecture Notes in Computer Science(), vol 11726. Springer, Cham. https://doi.org/10.1007/978-3-030-29765-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29765-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29764-0

  • Online ISBN: 978-3-030-29765-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics