Artificial Intelligence
On principle-based evaluation of extension-based argumentation semantics
Artificial Intelligence
Comparing Argumentation Semantics with Respect to Skepticism
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Skepticism relations for comparing argumentation semantics
International Journal of Approximate Reasoning
Resolution-Based Argumentation Semantics
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
A Systematic Classification of Argumentation Frameworks where Semantics Agree
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
On the resolution-based family of abstract argumentation semantics and its grounded instance
Artificial Intelligence
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Analyzing argumentation semantics with respect to the notion of skepticism is an important issue for developing general and well-founded comparisons among existing approaches. In this paper, we show that the notion of skepticism plays also a significant role in order to better understand the behavior of a specific semantics in different situations. Building on an articulated classification of argument justification states into seven distinct classes and on the definition of a weak and a strong version of skepticism relation, we define the property of skepticism adequacy of an argumentation semantics, which basically consists in requiring a lesser commitment when transforming a unidirectional attack into a mutual one. We then verify the skepticism adequacy of some literature proposals and obtain the rather surprising result that some semantics fail to satisfy this basic property.