On Warranted Inference in Argument Trees Based Framework

  • Safa Yahi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6929)


In this paper, we focus on logical argumentation introduced by Besnard and Hunter. First, we consider the so-called warranted inference which is based on the dialectical principle that is widely used in the literature of argumentatation. More precisely, we compare warranted inference with respect to the most frequently used coherence based approaches from flat belief bases in terms of productivity. It turns out that warranted inference is incomparable, w.r.t. productivity, with almost the coherence based approaches considered in this paper. Also, although too productive in some situations, warranted inference does not entail some very desirable conclusions which correspond to those which can be entailed from each consistent formula. Then, we introduce a new inference relation where the key idea is that the support of a counter-argument must not entail the conclusion of the objected argument which is quite intuitive. We show then that this inference relation ensures the inference of the previous desirable conclusions. Besides, we suggest to distinguish two levels of attacks: strong attacks and weak attacks. We propose then to weight our new inference relation based on the structure of the argument tree and also by taking into account the level strength of attacks.


Belief Base Argumentation Framework Inference Relation Attack Relation Argument Tree 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amgoud, L., Besnard, P.: A formal analysis of logic-based argumentation systems. In: Deshpande, A., Hunter, A. (eds.) SUM 2010. LNCS, vol. 6379, pp. 42–55. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Amgoud, L., Cayrol, C.: Inferring from inconsistency in preference-based argumentation frameworks. J. Autom. Reasoning 29(2), 125–169 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Amgoud, L., Dimopoulos, Y., Moraitis, P.: A unified and general framework for argumentation-based negotiation. In: AAMAS, p. 158 (2007)Google Scholar
  4. 4.
    Benferhat, S., Dubois, D., Prade, H.: Argumentative inference in uncertain and inconsistent knowledge bases. In: UAI, pp. 411–419 (1993)Google Scholar
  5. 5.
    Besnard, P., Hunter, A.: A logic-based theory of deductive arguments. Artificial Intelligence. 128(1-2), 203–235 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Besnard, P., Hunter, A.: Comparing and Rationalizing Arguments. In: Elements of Argumentation. MIT Press, Cambridge (2008)CrossRefGoogle Scholar
  7. 7.
    Besnard, P., Hunter, A.: Logical Argumentation. In: Elements of Argumentation. The MIT Press, Cambridge (2008)CrossRefGoogle Scholar
  8. 8.
    Besnard, P., Hunter, A.: Argumentation based on classical logic. In: Rahwan, I., Simari, G. (eds.) Argumentation in Artificial Intelligence (2009)Google Scholar
  9. 9.
    Cayrol, C.: On the relation between argumentation and non-monotonic coherence-based entailment. In: IJCAI, pp. 1443–1448 (1995)Google Scholar
  10. 10.
    Cayrol, C., Lagasquie-Schiex, M.-C.: Non-monotonic syntax-based entailment: A classification of consequence relations. In: Froidevaux, C., Kohlas, J. (eds.) ECSQARU 1995. LNCS, vol. 946, pp. 107–114. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  11. 11.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–358 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    García, A.J., Simari, G.R.: Defeasible logic programming: an argumentative approach. Theory Pract. Log. Program. 4, 95–138 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Gorogiannis, N., Hunter, A.: Instantiating abstract argumentation with classical logic arguments: Postulates and properties. Artif. Intell. 175(9-10), 1479–1497 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Hirsch, R., Gorogiannis, N.: The complexity of the warranted formula problem in propositional argumentation. J. Log. Comput. 20(2), 481–499 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Kakas, A.C., Moraitis, P.: Argumentation based decision making for autonomous agents. In: AAMAS, pp. 883–890 (2003)Google Scholar
  16. 16.
    Lagasquie-Schiex, M.-C.: Contribution à l’étude des relations d’inférence non-monotone combinant inférence classique et préférences. Thèse de doctorat, Université Paul Sabatier, Toulouse, France (Décembre 1995)Google Scholar
  17. 17.
    Martinez, M.V., Hunter, A.: Incorporating classical logic argumentation into policy-based inconsistency management in relational databases. In: The Uses of Computational Argumentation Symposium, AAAI 2009 Fall Symposium Series (2009)Google Scholar
  18. 18.
    Pinkas, G., Loui, R.P.: Reasoning from inconsistency: A taxonomy of principles for resolving conflict. In: KR, pp. 709–719 (1992)Google Scholar
  19. 19.
    Pollock, J.L.: How to reason defeasibly. Artif. Intell. 57(1), 1–42 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Resher, N., Manor, R.: On inference from inconsistent premises. Theory and Decision 1, 179–219 (1970)CrossRefGoogle Scholar
  21. 21.
    Verheij, B.: Automated argument assistance for lawyers. In: ICAIL, pp. 43–52 (1999)Google Scholar
  22. 22.
    Vreeswijk, G.: Abstract argumentation systems. Artif. Intell. 90(1-2), 225–279 (1997)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Safa Yahi
    • 1
  1. 1.LSIS-CNRS, UMR 6168 IUT d’Aix-en-ProvenceUniversité de la MéditerranéeFrance

Personalised recommendations