La argumentación abstracta en Inteligencia Artificial: problemas de interpretación y adecuación de las semánticas para la toma de decisiones

The abstract argumentation frameworks model is currently the most used tool for characterizing the justification of defeasible arguments in Artificial Intelligence. Justifications are determined on a given attack relation among arguments and are formalized as extension semantics. In this work we argue that, contrariwise to the assumptions in that model, either some argumentation frameworks are meaningless under certain concrete definitions of the attack relation, or some of the most used extension semantics in the literature, based on the defense notion of admissibility, are not suitable in particular for the justification of arguments for decision making