Constrained Assumption-Based Argumentation Frameworks
2026-02-13 • Artificial Intelligence
Artificial IntelligenceLogic in Computer Science
AI summaryⓘ
The authors focus on Assumption-based Argumentation (ABA), a way to structure arguments, which usually only works with simple, fixed statements. They introduce constrained ABA (CABA), allowing arguments to include variables that can represent many values, extending the system to more complex situations. They define new ways to interpret attacks between these more flexible arguments and show that their approach includes the original ABA as a special case.
Assumption-based ArgumentationStructured ArgumentationConstrained VariablesNon-ground SemanticsArgument AttacksPropositional AtomsVariable DomainsConservative Generalisation
Authors
Emanuele De Angelis, Fabio Fioravanti, Maria Chiara Meo, Alberto Pettorossi, Maurizio Proietti, Francesca Toni
Abstract
Assumption-based Argumentation (ABA) is a well-established form of structured argumentation. ABA frameworks with an underlying atomic language are widely studied, but their applicability is limited by a representational restriction to ground (variable-free) arguments and attacks built from propositional atoms. In this paper, we lift this restriction and propose a novel notion of constrained ABA (CABA), whose components, as well as arguments built from them, may include constrained variables, ranging over possibly infinite domains. We define non-ground semantics for CABA, in terms of various notions of non-ground attacks. We show that the new semantics conservatively generalise standard ABA semantics.