We review the First International Competition on Computational Models of Argumentation (ICCMA'15). The competition evaluated submitted solvers' performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers. While still a young field when compared to areas such as SAT solving and logic programming, the argumentation community is very active, with a conference series (COMMA, which began in 2006) and a variety of workshops and special issues of journals.
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.
In this report from the field we describe jArgSemSAT, a Java re-implementation of ArgSemSAT. We show that jArgSemSAT can be easily integrated in existing argumentation systems (1) as an off-the-shelf, standalone, library; (2) as a Tweety compatible library; and (3) as a fast and robust web service freely available on the Web. The performance section shows that — despite being written in Java — jArgSemSAT is very efficient w.r.t. preferred semantics, which has associated problems with high computational complexity.
Abstract argumentation frameworks (AFs) are one of the central formalisms in AI; equipped with a wide range of semantics, they have proven useful in several application domains. In the thesis we want to complete and extend the recent study on expressiveness of argumentation semantics and use these and other theoretical results for implementations of reasoning tasks in AFs. Moreover, we plan to utilize results on realizability in dynamic scenarios of abstract argumentation, such as revision of argumentation frameworks. Hereby, the knowledge of which extensions can occur together is of central interest when trying to achieve a certain outcome. In other words, the ultimate goal of the thesis is to gain theoretical insights on argumentation semantics in order to employ them in practically efficient reasoning systems for both the evaluation and evolution of AFs.
This paper presents a novel SAT-based approach for the computation of extensions in abstract argumentation, with focus on preferred semantics, and an empirical evaluation of its performances. The approach is based on the idea of reducing the problem of computing complete extensions to a SAT problem and then using a depth-first search method to derive preferred extensions. The proposed approach has been tested using two distinct SAT solvers and compared with three state-of-the-art systems for preferred extension computation. It turns out that the proposed approach delivers significantly better performances in the large majority of the considered cases.