On scaling the enumeration of the preferred extensions of abstract argumentation frameworks

Abstract

Argumentation has emerged as one of the major fields in Artificial Intelligence. In particular, Dung's abstract argumentation framework (AF) has received much attention in the last two decades, and many computational issues have been investigated for different argumentation semantics. Specifically, enumerating the sets of arguments (i.e., extensions) prescribed by an argumentation semantics is arguably one of the most challenging problems for AFs, and this is particularly the case for the well-known preferred semantics. In this paper, we propose an algorithm for efficiently computing the set of preferred extensions of a given AF. Our technique relies on first computing the ideal extension for the given AF, and then using it to prune some arguments so that a smaller AF is obtained. Finally, we use state-of-the-art solvers for enumerating the preferred extensions of the pruned AF, and then return the extensions of the input AF after obtaining them from those of the pruned AF. We experimentally compared our technique with the solver that won the track on enumerating preferred extensions of the last International Competition on Computational Models of Argumentation, and found that our approach is orders of magnitude faster than the computation from scratch.