不确定规则与前提下结构化论证框架的表达力研究结果
Expressivity Results for Structured Argumentation Frameworks with Uncertain Rules and Premises
AI 导读
一项发表于 Journal of Artificial Intelligence Research 87(2026年10月)的研究提出适用于抽象与结构化论证形式的比较表达力概念,将论证的定性不确定性根植于规则与前提两个组件。作者证明了两类结果:抽象侧涉及不完备抽象论证框架及其依赖扩展,结构侧涉及 ASPIC$^+$、假设论证(ABA)与演绎论证,比较了带不确定性的抽象与结构化论证模型的表达力。
正文 · 原文
Abstract:Modelling qualitative uncertainty in formal argumentation is essential both for practical applications and theoretical understanding. Yet, most existing work focuses on abstract models of argumentation under 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 root the uncertainty of arguments in their components in structured models: rules and premises. Our main conceptual contribution is to provide an intuitive notion of comparative expressivity suitable for both abstract and structured formalisms. Roughly, one formalism for arguing with qualitative uncertainty is at least as expressive as another if the former can generate the same set of abstract argumentation frameworks (completions) as the latter. On the technical side, we prove negative and positive results comparing the expressivity of abstract and structured models of argumentation with uncertainty. These results concern incomplete abstract argumentation frameworks and their extension with dependencies on the abstract side, and ASPIC$^+$, assumption-based argumentation (ABA) and deductive argumentation on the structured side.
| Subjects: | Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO) |
| MSC classes: | 03B60 |
| Cite as: | arXiv:2510.18631 [cs.AI] |
| (or arXiv:2510.18631v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2510.18631 arXiv-issued DOI via DataCite |
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| Journal reference: | Journal of Artificial Intelligence Research 87, Article 15 (October 2026) |
| Related DOI: | https://doi.org/10.1613/jair.1.21969
DOI(s) linking to related resources |
Submission history
From: Antonio Yuste-Ginel [view email]
[v1]
Tue, 21 Oct 2025 13:36:38 UTC (73 KB)
[v2]
Tue, 17 Feb 2026 15:58:03 UTC (72 KB)
[v3]
Fri, 23 Oct 2026 12:43:43 UTC (123 KB)
来源:arXiv cs.AI · arxiv.org