Double Rectified Linear Unit-based Modular Semantics for Quantitative Bipolar Argumentation Framework

2026-05-04

AI Summary

This work addresses the issue that existing semantics for quantitative bipolar argumentation frameworks (QBAFs) often yield counterintuitive acceptability assessments even in acyclic settings. To remedy this, the paper proposes a modular gradual semantics grounded in the dual rectified linear unit (DReLU), which unifies the modeling of attack and support relations and iteratively updates argument strength. The proposed approach not only satisfies established rationality postulates but also significantly enhances the intuitive plausibility of outcomes. Notably, it is the first semantics proven to converge for both acyclic and cyclic QBAFs. Empirical evaluation demonstrates that the new semantics consistently produces acceptability degrees across multiple benchmark cases that align more closely with human intuition.

Abstract

Quantitative Bipolar Argumentation Frameworks (QBAFs) provide an alternative approach to computing argument acceptability in Bipolar Argumentation Frameworks (BAFs). Each argument is assigned an initial strength, which is then updated to a final strength by considering the influence of both its attackers and supporters. Over the years, several semantics have been proposed to compute argument acceptability in QBAFs, yet they often yield divergent or counterintuitive results, even for simple acyclic cases. We introduce novel gradual semantics for QBAFs that address these limitations, producing results that align more closely with intuitive expectations, while satisfying established rationality postulates from the literature. Furthermore, we study its convergence behavior, proving that it converges not only for acyclic QBAFs but also for broader classes of cyclic frameworks.

Problem

• Quantitative Bipolar Argumentation Frameworks
• argument acceptability
• gradual semantics
• rationality postulates
• convergence

Innovation

• Double Rectified Linear Unit
• Modular Semantics
• Quantitative Bipolar Argumentation Framework
• Gradual Semantics
• Convergence