On the combination of argumentation solvers into parallel portfolios

In the light of the increasing interest in efficient algorithms for solving abstract argumentation problems and the pervasive availability of multicore machines, a natural research issue is to combine existing argumentation solvers into parallel portfolios. In this work, we introduce six methodologies for the automatic configuration of parallel portfolios of argumentation solvers for enumerating the preferred extensions of a given framework. In particular, four methodologies aim at combining solvers in static portfolios, while two methodologies are designed for the dynamic configuration of parallel portfolios. Our empirical results demonstrate that the configuration of parallel portfolios is a fruitful way for exploiting multicore machines, and that the presented approaches outperform the state of the art of parallel argumentation solvers

An Efficient Java-Based Solver for Abstract Argumentation Frameworks: jArgSemSAT

Dung’s argumentation frameworks are adopted in a variety of applications, from argument-mining, to intelligence analysis and legal reasoning. Despite this broad spectrum of already existing applications, the mostly adopted solver—in virtue of its simplicity—is far from being comparable to the current state-of-the-art solvers. On the other hand, most of the current state-of-the-art solvers are far too complicated to be deployed in real-world settings. In this paper we provide and extensive description of jArgSemSAT, a Java re-implementation of ArgSemSAT. ArgSemSAT represents the best single solver for argumentation semantics with the highest level of computational complexity. 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. Our large experimental analysis shows that—despite being written in Java—jArgSemSAT would have scored in most of the cases among the three bests solvers for the two semantics with highest computational complexity—Stable and Preferred—in the last competition on computational models of argumentation

Argumentation Frameworks Features: an Initial Study

Semantics extensions are the outcome of the argumentation reasoning process: enumerating them is generally an intractable problem. For preferred semantics two efficient algorithms have been recently proposed, PrefSAT and SCC-P, with significant runtime variations. This preliminary work aims at investigating the reasons (argumentation framework features) for such variations. Remarkably, we observed that few features have a strong impact, and those exploited by the most performing algorithm are not the most relevant

How we designed winning algorithms for abstract argumentation and which insight we attained

In this paper we illustrate the design choices that led to the development of ArgSemSAT, the winner of the preferred semantics track at the 2017 International Competition on Computational Models of Arguments (ICCMA 2017), a biennial contest on problems associated to the Dung’s model of abstract argumentation frameworks, widely recognised as a fundamental reference in computational argumentation. The algorithms of ArgSemSAT are based on multiple calls to a SAT solver to compute complete labellings, and on encoding constraints to drive the search towards the solution of decision and enumeration problems. In this paper we focus on preferred semantics (and incidentally stable as well), one of the most popular and complex semantics for identifying acceptable arguments. We discuss our design methodology that includes a systematic exploration and empirical evaluation of labelling encodings, algorithmic variations and SAT solver choices. In designing the successful ArgSemSAT, we discover that: (1) there is a labelling encoding that appears to be universally better than other, logically equivalent ones; (2) composition of different techniques such as AllSAT and enumerating stable extensions when searching for preferred semantics brings advantages; (3) injecting domain specific knowledge in the algorithm design can lead to significant improvements

Exploiting Parallelism for Hard Problems in Abstract Argumentation

Abstract argumentation framework (AF) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to AF s are missing, thus potentially limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup