Design and Results of the Second International Competition on Computational Models of Argumentation

Within AI, several sub-fields are particularly relevant to - and benefit from - studies of argumentation. These include decision support, knowledge representation, nonmonotonic reasoning, and multiagent systems. Moreover, computational argumentation provides a formal investigation of problems that have been studied informally only by philosophers, and which consequently allow for the development of computational tools for argumentation, see (Atkinson et al., 2017). Since its invention by Dung (1995), abstract argumentation based on argumentation frameworks (AFs) has become a key concept for the field. In AFs, argumentation scenarios are modeled as simple directed graphs, where the vertices represent arguments and each edge corresponds to an attack between two arguments. Besides its simplicity, there are several reasons for the success story of this concept: First, a multitude of semantics (Baroni et al., 2011, 2018) allows for tight coupling of argumentation with existing formalisms from the areas of knowledge representation and logic programming; indeed, one of the main motivations of Dung's work (Dung, 1995) was to give a uniform representation of several nonmonotonic formalisms including Reiter's Default Logic, Pollock's Defeasible Logic, and Logic Programming (LP) with default negation; the latter lead to a series of works that investigated the relationship between different LP semantics and different AF semantics, see e.g.