Argumentation-Based Paraconsistent Logics

  • Jonathan Ben-Naim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8577)


Argumentation is a promising approach for reasoning with inconsistent information. Starting from a knowledge base encoded in a logical language, an argumentation system defines arguments and attacks between them using the consequence operator associated with the language. Finally, it uses a semantics for evaluating the arguments. The plausible conclusions to be drawn from the knowledge base are those supported by “good” arguments.

In this paper, we discuss two families of such systems: the family using extension semantics and the one using ranking semantics. We discuss the outcomes of both families and compare them.


Knowledge Base Consequence Operator Argumentation Framework Paraconsistent Logic Argumentation System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Amgoud, L., Besnard, P.: Logical limits of abstract argumentation frameworks. Journal of Applied Non-Classical Logics 23(3), 229–267 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Baroni, P., Giacomin, M., Guida, G.: Scc-recursiveness: A general schema for argumentation semantics. Artificial Intelligence Journal 168, 162–210 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Belnap, N.D.: A Useful Four-Valued Logic. In: Dunn, J., Epstein, G. (eds.) Modern Uses of Multiple-Valued Logic, pp. 7–37. Oriel Press (1977)Google Scholar
  5. 5.
    Cholvy, L.: Automated reasoning with merged contradictory information whose reliability depends on topics. In: Froidevaux, C., Kohlas, J. (eds.) ECSQARU 1995. LNCS, vol. 946, pp. 125–132. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  6. 6.
    D’Ottaviano, I., da Costa, N.: Sur un problème de Jaśkowski. In: Comptes Rendus de l’Académie des Sciences de Paris, vol. 270, pp. 1349–1353 (1970)Google Scholar
  7. 7.
    Dung, P., Mancarella, P., Toni, F.: Computing ideal skeptical argumentation. Artificial Intelligence Journal 171, 642–674 (2007)zbMATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Dung, P.M.: On the Acceptability of Arguments and its Fundamental Role in Non-Monotonic Reasoning, Logic Programming and n-Person Games. AIJ 77, 321–357 (1995)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Kleer, J.D.: Using crude probability estimates to guide diagnosis. Artificial Intelligence 45, 381–391 (1990)CrossRefGoogle Scholar
  10. 10.
    Reiter, R.: A logic for default reasoning. Artificial Intelligence 13(1-2), 81–132 (1980)zbMATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Rescher, N., Manor, R.: On inference from inconsistent premises. Journal of Theory and Decision 1, 179–219 (1970)zbMATHCrossRefGoogle Scholar
  12. 12.
    Tarski, A.: On Some Fundamental Concepts of Metamathematics. In: Woodger, E.H. (ed.) Logic, Semantics, Metamathematics. Oxford Uni. Press (1956)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jonathan Ben-Naim
    • 1
  1. 1.IRIT – CNRSToulouse Cedex 09France

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