Comparative Expressivity for Structured Argumentation Frameworks with Uncertain Rules and Premises

• URL: http://arxiv.org/abs/2510.18631v1
• Date: Tue, 21 Oct 2025 13:36:38 GMT
• Title: Comparative Expressivity for Structured Argumentation Frameworks with Uncertain Rules and Premises
• Authors: Carlo Proietti, Antonio Yuste-Ginel,
• Abstract summary: We study plausible instantiations of abstract models structured within rules and premises.<n>Our main technical contributions are the introduction of a notion of expressivity that can handle abstract and structured formalisms.
• Score: 0.7967000209136494
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Causal and Compositional Abstraction [0.0]
  We present a general account of abstractions between low and high level models as natural transformations.<n>This provides a new formalisation of causal abstraction, unifying several notions in the literature.<n>We show that abstraction can be generalised to further compositional models.
  arXiv  Detail & Related papers  (2026-02-18T17:06:09Z)
• Abstract Activation Spaces for Content-Invariant Reasoning in Large Language Models [28.102903742881576]
  We introduce a framework for abstraction-guided reasoning that explicitly separates structural inference from lexical semantics.<n>We show that abstraction-aligned steering reduces content-driven errors and improves validity-sensitive performance.
  arXiv  Detail & Related papers  (2026-02-02T18:48:44Z)
• Abstract Argumentation with Subargument Relations [0.8460698440162889]
  Dung's abstract argumentation framework characterises argument acceptability solely via an attack relation.<n>This framework limits the ability to represent structural dependencies that are central in many structured argumentation formalisms.<n>We study abstract argumentation frameworks enriched with an explicit subargument relation, treated alongside attack as a basic relation.<n>This framework provides a principled abstraction of structural information and clarifies the role of subarguments in abstract acceptability reasoning.
  arXiv  Detail & Related papers  (2026-01-17T12:54:10Z)
• Schoenfeld's Anatomy of Mathematical Reasoning by Language Models [56.656180566692946]
  We adopt Schoenfeld's Episode Theory as an inductive, intermediate-scale lens and introduce ThinkARM (Anatomy of Reasoning in Models)<n>ThinkARM explicitly abstracts reasoning traces into functional reasoning steps such as Analysis, Explore, Implement, verify, etc.<n>We show that episode-level representations make reasoning steps explicit, enabling systematic analysis of how reasoning is structured, stabilized, and altered in modern language models.
  arXiv  Detail & Related papers  (2025-12-23T02:44:25Z)
• Causal Abstractions, Categorically Unified [9.341830361844337]
  We present a categorical framework for relating causal models that represent the same system at different levels of abstraction.<n>Our approach unifies and generalizes previously considered causal abstractions.<n>We discuss how methods from mechanistic interpretability, such as circuit analysis and sparse autoencoders, fit within our framework.
  arXiv  Detail & Related papers  (2025-10-06T17:09:30Z)
• Causal Abstraction Inference under Lossy Representations [53.18851962820361]
  We introduce a new type of abstractions called projected abstractions that generalize existing definitions to accommodate lossy representations.<n>We show how to construct a projected abstraction from the low-level model and how it translates equivalent observational, interventional, and counterfactual causal queries from low to high-level.
  arXiv  Detail & Related papers  (2025-09-25T21:20:42Z)
• Causal Abstraction in Model Interpretability: A Compact Survey [5.963324728136442]
  causal abstraction provides a principled approach to understanding and explaining the causal mechanisms underlying model behavior. This survey paper delves into the realm of causal abstraction, examining its theoretical foundations, practical applications, and implications for the field of model interpretability.
  arXiv  Detail & Related papers  (2024-10-26T12:24:28Z)
• Conceptual and Unbiased Reasoning in Language Models [98.90677711523645]
  We propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions. We show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making.
  arXiv  Detail & Related papers  (2024-03-30T00:53:53Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.<n>We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.<n>Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• Integrating Temporality and Causality into Acyclic Argumentation Frameworks using a Transition System [0.5420492913071214]
  We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language. We establish causal relationships between the enunciation of arguments and their consequences, whether direct or indirect. An Answer Set Programming implementation is also proposed, as well as perspectives towards explanations.
  arXiv  Detail & Related papers  (2023-03-16T10:13:47Z)
• Admissibility in Strength-based Argumentation: Complexity and Algorithms (Extended Version with Proofs) [1.5828697880068698]
  We study the adaptation of admissibility-based semantics to Strength-based Argumentation Frameworks (StrAFs) Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung's fundamental lemma. We propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions.
  arXiv  Detail & Related papers  (2022-07-05T18:42:04Z)
• A Formalisation of Abstract Argumentation in Higher-Order Logic [77.34726150561087]
  We present an approach for representing abstract argumentation frameworks based on an encoding into classical higher-order logic. This provides a uniform framework for computer-assisted assessment of abstract argumentation frameworks using interactive and automated reasoning tools.
  arXiv  Detail & Related papers  (2021-10-18T10:45:59Z)
• Exploring Discourse Structures for Argument Impact Classification [48.909640432326654]
  This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. We propose DisCOC to inject and fuse the sentence-level structural information with contextualized features derived from large-scale language models.
  arXiv  Detail & Related papers  (2021-06-02T06:49:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.