Skip to main content
SpringerLink
Log in
Book cover

Brazilian Conference on Intelligent Systems

BRACIS 2021: Intelligent Systems pp 344–358Cite as

On the Refinement of Compensation-Based Semantics for Weighted Argumentation Frameworks

  • Conference paper
  • First Online:

  • 551 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13073))

Abstract

Acceptability semantics for the frameworks of weighted argumentation can satisfy up to one of the principles of (Quality Precedence), (Cardinality Precedence) or (Compensation), which are pairwise incompatible. In this paper we define two new principles: (Quality Compensation) and (Cardinality Compensation), which are weakened versions of (Quality Precedence) and (Cardinality Precedence), respectively. We show that these new principles are compatible with (Compensation) and propose two new semantics: a t-conorm-based, which can satisfy (Quality Compensation) and a cumulative sum-based semantics, which satisfies (Cardinality Compensation).

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
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

Learn about institutional subscriptions

References

  1. Adeli, H., Hung, S.L.: Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems. Wiley, Hoboken (1994)

    Google Scholar 

  2. Amgoud, L., Ben-Naim, J.: Ranking-based semantics for argumentation frameworks. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds.) SUM 2013. LNCS (LNAI), vol. 8078, pp. 134–147. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40381-1_11

    Chapter  Google Scholar 

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

    Google Scholar 

  4. Amgoud, L., Ben-Naim, J., Doder, D., Vesic, S.: Acceptability semantics for weighted argumentation frameworks. In: Twenty-Sixth International Joint Conference on Artificial Intelligence (2017)

    Google Scholar 

  5. Amgoud, L., Besnard, P.: Bridging the gap between abstract argumentation systems and logic. In: Godo, L., Pugliese, A. (eds.) SUM 2009. LNCS (LNAI), vol. 5785, pp. 12–27. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04388-8_3

    Chapter  Google Scholar 

  6. Amgoud, L., Cayrol, C.: A reasoning model based on the production of acceptable arguments. Ann. Math. Artif. Intell. 34(1), 197–215 (2002)

    Article  MathSciNet  Google Scholar 

  7. Amgoud, L., Maudet, N., Parsons, S.: Modelling dialogues using argumentation. In: Proceedings Fourth International Conference on MultiAgent Systems, pp. 31–38. IEEE (2000)

    Google Scholar 

  8. Amgoud, L., Prade, H.: Using arguments for making and explaining decisions. Artif. Intell. 173(3), 413–436 (2009)

    Article  MathSciNet  Google Scholar 

  9. Baroni, P., Giacomin, M.: On principle-based evaluation of extension-based argumentation semantics. Artif. Intell. 171(10–15), 675–700 (2007)

    Article  MathSciNet  Google Scholar 

  10. Baroni, P., Romano, M., Toni, F., Aurisicchio, M., Bertanza, G.: Automatic evaluation of design alternatives with quantitative argumentation. Argum. Comput. 6(1), 24–49 (2015)

    Article  Google Scholar 

  11. Bench-Capon, T.J.: Persuasion in practical argument using value-based argumentation frameworks. J. Log. Comput. 13(3), 429–448 (2003)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  13. da Costa Pereira, C., Tettamanzi, A.G., Villata, S.: Changing one’s mind: erase or rewind? possibilistic belief revision with fuzzy argumentation based on trust. In: IJCAI, International Joint Conference on Artificial Intelligence (2011)

    Google Scholar 

  14. De Baets, B., Kerre, E.: Fuzzy relations and applications. In: Advances in Electronics and Electron Physics, vol. 89, pp. 255–324. Elsevier (1994)

    Google Scholar 

  15. Deschrijver, G., Cornelis, C., Kerre, E.E.: On the representation of intuitionistic fuzzy t-norms and t-conorms. IEEE Trans. Fuzzy Syst. 12(1), 45–61 (2004)

    Article  Google Scholar 

  16. Deschrijver, G., Kerre, E.E.: On the composition of intuitionistic fuzzy relations. Fuzzy Sets Syst. 136(3), 333–361 (2003)

    Article  MathSciNet  Google Scholar 

  17. Dubois, D., Fargier, H., Bonnefon, J.F.: On the qualitative comparison of decisions having positive and negative features. J. Artif. Intell. Res. 32, 385–417 (2008)

    Article  MathSciNet  Google Scholar 

  18. Gabbay, D.M., Rodrigues, O.: Equilibrium states in numerical argumentation networks. Logica Universalis 9(4), 411–473 (2015)

    Article  MathSciNet  Google Scholar 

  19. Giannini, F., Marra, G., Diligenti, M., Maggini, M., Gori, M.: Learning and t-norms theory. arXiv preprint arXiv:1907.11468 (2019)

  20. Grzegorzewski, P., Mrówka, E.: Soft querying via intuitionistic fuzzy sets. In: Proceedings of the 9th International conference on Information Processing and management of Uncertainty in Knowledge-Based Systems IMPU 2002. Citeseer (2002)

    Google Scholar 

  21. Hunter, A.: A probabilistic approach to modelling uncertain logical arguments. Int. J. Approx. Reason. 54(1), 47–81 (2013)

    Article  MathSciNet  Google Scholar 

  22. Kakas, A., Moraitis, P.: Argumentation based decision making for autonomous agents. In: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 883–890 (2003)

    Google Scholar 

  23. Klement, E.P., Mesiar, R.: Logical, Algebraic, Analytic and Probabilistic Aspects of Triangular Norms. Elsevier Science B.V, Amsterdam (2005)

    Google Scholar 

  24. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms, 1st edn. Springer, Netherlands (2000). https://doi.org/10.1007/978-94-015-9540-7

    Book  MATH  Google Scholar 

  25. Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 157–166 (2005)

    Google Scholar 

  26. Li, H., Oren, N., Norman, T.J.: Probabilistic argumentation frameworks. In: Modgil, S., Oren, N., Toni, F. (eds.) TAFA 2011. LNCS (LNAI), vol. 7132, pp. 1–16. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29184-5_1

    Chapter  Google Scholar 

  27. Lorenz, M.O.: Methods of measuring the concentration of wealth. Publ. Am. Stat. Assoc. 9(70), 209–219 (1905)

    Google Scholar 

  28. Mizumoto, M.: Pictorial representations of fuzzy connectives, part I: cases of t-norms, t-conorms and averaging operators. Fuzzy Sets Syst. 31(2), 217–242 (1989)

    Article  Google Scholar 

  29. Modgil, S.: Reasoning about preferences in argumentation frameworks. Artif. Intell. 173(9–10), 901–934 (2009)

    Article  MathSciNet  Google Scholar 

  30. Prakken, H.: Formal systems for persuasion dialogue. Knowl. Eng. Rev. 21(2), 163 (2006)

    Article  Google Scholar 

  31. Rago, A., Toni, F., Aurisicchio, M., Baroni, P.: Discontinuity-free decision support with quantitative argumentation debates (2016)

    Google Scholar 

  32. Thimm, M.: A probabilistic semantics for abstract argumentation. In: ECAI, vol. 12, pp. 750–755 (2012)

    Google Scholar 

  33. Yager, R.R., Kreinovich, V.: Universal approximation theorem for uninorm-based fuzzy systems modeling. Fuzzy Sets Syst. 140(2), 331–339 (2003)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Rodovia CE-040, Km 137, 1 s/n, Aracati, CE, Brazil

    Henrique Viana

  2. Departamento de Ciência da Computação, Universidade Federal do Ceará (UFC), P.O. Box 12166, Fortaleza, CE, 60455-760, Brazil

    João Alcântara

Authors
  1. Henrique Viana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Alcântara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henrique Viana .

Editor information

Editors and Affiliations

  1. Universidade Federal de Sergipe, São Cristóvão, Brazil

    André Britto

  2. Universidade de São Paulo, São Paulo, Brazil

    Karina Valdivia Delgado

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Viana, H., Alcântara, J. (2021). On the Refinement of Compensation-Based Semantics for Weighted Argumentation Frameworks. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13073. Springer, Cham. https://doi.org/10.1007/978-3-030-91702-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91702-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91701-2

  • Online ISBN: 978-3-030-91702-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation