Abstract
Argumentation is a reasoning model based on the construction and the evaluation of arguments. In his seminal paper, Dung has proposed the most abstract argumentation framework. In that framework, arguments are assumed to have the same strength. This assumption is unfortunately strong and often unsatisfied. Consequently, three extensions of the framework have been proposed in the literature. The first one assumes that an argumentation framework should be equipped with a (partial or total) preorder representing a preference relation between arguments, and capturing a difference of strengths of the arguments. The source of this preference relation is not specified, thus it can be instantiated in different manners. The second extension claims that the strength of an argument depends on the value(s) promoted by this argument. The third extension states that the set of arguments is equipped with several preorders; each of them expresses preferences between arguments in a given context.
The contribution of this paper is two-fold: first, it proposes a comparative study of these extensions of Dung’s framework. It clearly shows under which conditions two proposals are equivalent. The second contribution of the paper consists in integrating the three extensions into a common more expressive framework.
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References
Amgoud, L., Cayrol, C.: A reasoning model based on the production of acceptable arguments. Annals of Mathematics and Artificial Intelligence 34, 197–216 (2002)
Amgoud, L., Parsons, S., Perrussel, L.: An argumentation framework based on contextual preferences. In: Proceedings of the International Conference on Formal and Applied and Practical Reasoning (FAPR 2000), pp. 59–67 (2000)
Amgoud, L., Prade, H.: Using arguments for making and explaining decisions. Artif. Intell. 173(3-4), 413–436 (2009)
Bench-Capon, T.J.M.: Persuasion in practical argument using value-based argumentation frameworks. Journal of Logic and Computation 13(3), 429–448 (2003)
Bonet, B., Geffner, H.: Arguing for decisions: A qualitative model of decision making. In: Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (UAI 1996), pp. 98–105 (1996)
Bourre, J.-M., Bégat, A., Leroux, M.-C., Mousques-Cami, V., Pérandel, N., Souply, F.: Valeur nutritionnelle (macro et micro-nutriments) de farines et pains français. Médecine et Nutrition 44(2), 49–76 (2008)
Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence Journal 77, 321–357 (1995)
Fox, J., Das, S.: Safe and Sound. Artificial Intelligence in Hazardous Applications. AAAI Press, The MIT Press (2000)
Kaci, S., van der Torre, L.: Preference-based argumentation: Arguments supporting multiple values. Int. J. Approx. Reasoning 48(3), 730–751 (2008)
Kraus, S., Sycara, K., Evenchik, A.: Reaching agreements through argumentation: a logical model and implementation 104, 1–69 (1998)
Perelman, C.: Justice, Law and Argument. Reidel, Dordrecht (1980)
Sycara, K.: Persuasive argumentation in negotiation. Theory and Decision 28, 203–242 (1990)
Yager, R.R.: Entropy and specificity in a mathematical theory of evidence. In: Classic Works of the Dempster-Shafer Theory of Belief Functions, pp. 291–310 (2008)
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Bourguet, JR., Amgoud, L., Thomopoulos, R. (2010). Towards a Unified Model of Preference-Based Argumentation. In: Link, S., Prade, H. (eds) Foundations of Information and Knowledge Systems. FoIKS 2010. Lecture Notes in Computer Science, vol 5956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11829-6_21
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DOI: https://doi.org/10.1007/978-3-642-11829-6_21
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