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[2510.18631] Comparative Expressivity for Structured Argumentation Frameworks with Uncertain Rules and Premises
Summary
This paper explores the expressivity of structured argumentation frameworks that incorporate uncertainty, presenting both theoretical and practical implications for argumentation models.
Why It Matters
Understanding how to model qualitative uncertainty in argumentation is crucial for both theoretical advancements and practical applications in artificial intelligence. This research addresses a gap in existing literature by comparing abstract and structured argumentation frameworks, providing insights that could enhance decision-making processes in AI systems.
Key Takeaways
- Introduces a new notion of expressivity for argumentation frameworks.
- Compares the expressivity of abstract and structured models under uncertainty.
- Presents both negative and positive expressivity results.
- Impacts the understanding of incomplete abstract argumentation frameworks.
- Enhances the theoretical foundation for practical applications in AI.
Computer Science > Artificial Intelligence arXiv:2510.18631 (cs) [Submitted on 21 Oct 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Comparative Expressivity for Structured Argumentation Frameworks with Uncertain Rules and Premises Authors:Carlo Proietti, Antonio Yuste-Ginel View a PDF of the paper titled Comparative Expressivity for Structured Argumentation Frameworks with Uncertain Rules and Premises, by Carlo Proietti and 1 other authors View PDF HTML (experimental) Abstract:Modelling qualitative uncertainty in formal argumentation is essential both for practical applications and theoretical understanding. Yet, most of the existing works focus on \textit{abstract} models for arguing with uncertainty. Following a recent trend in the literature, we tackle the open question of studying plausible instantiations of these abstract models. To do so, we ground the uncertainty of arguments in their components, structured within rules and premises. Our main technical contributions are: i) the introduction of a notion of expressivity that can handle abstract and structured formalisms, and ii) the presentation of both negative and positive expressivity results, comparing the expressivity of abstract and structured models of argumentation with uncertainty. These results affect incomplete abstract argumentation frameworks, and their extension with dependencies, on the abstract side, and ASPIC+, on the structured side. Subjects: Artificial Intelligence (cs.AI); Logic in...
