ConArg: a Tool to Solve (Weighted) Abstract Argumentation Frameworks with (Soft) Constraints

TL;DR

ConArg is a constraint programming tool that models and solves various abstract argumentation problems, including weighted frameworks, using semiring-based soft constraints for flexible preference representation.

Contribution

This paper introduces ConArg, a novel constraint programming tool that efficiently handles both standard and weighted abstract argumentation frameworks with a unified approach.

Findings

ConArg can generate small-world networks for argumentation analysis.

It effectively finds various extensions like conflict-free, admissible, and stable.

Performance comparisons show competitive results with ASPARTIX.

Abstract

ConArg is a Constraint Programming-based tool that can be used to model and solve different problems related to Abstract Argumentation Frameworks (AFs). To implement this tool we have used JaCoP, a Java library that provides the user with a Finite Domain Constraint Programming paradigm. ConArg is able to randomly generate networks with small-world properties in order to find conflict-free, admissible, complete, stable grounded, preferred, semi-stable, stage and ideal extensions on such interaction graphs. We present the main features of ConArg and we report the performance in time, showing also a comparison with ASPARTIX [1], a similar tool using Answer Set Programming. The use of techniques for constraint solving can tackle the complexity of the problems presented in [2]. Moreover we suggest semiring-based soft constraints as a mean to parametrically represent and solve Weighted Argumentation Frameworks: different kinds of preference levels related to attacks, e.g., a score representing a "fuzziness", a "cost" or a probability, can be represented by choosing different instantiation of the semiring algebraic structure. The basic idea is to provide a common computational and quantitative framework.