A Methodology for Gradual Semantics for Structured Argumentation under Incomplete Information

• URL: http://arxiv.org/abs/2410.22209v1
• Date: Tue, 29 Oct 2024 16:38:35 GMT
• Title: A Methodology for Gradual Semantics for Structured Argumentation under Incomplete Information
• Authors: Antonio Rago, Stylianos Loukas Vasileiou, Francesca Toni, Tran Cao Son, William Yeoh,
• Abstract summary: We provide a novel methodology for obtaining gradual semantics for structured argumentation frameworks. Our methodology accommodates incomplete information about arguments' premises. We demonstrate the potential of our approach by introducing two different instantiations of the methodology.
• Score: 15.717458041314194
• License:
• Abstract: Gradual semantics have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper, we provide a novel methodology for obtaining gradual semantics for structured argumentation frameworks, where the building blocks of arguments and relations between them are known, unlike in QBAFs, where arguments are abstract entities. Differently from existing approaches, our methodology accommodates incomplete information about arguments' premises. We demonstrate the potential of our approach by introducing two different instantiations of the methodology, leveraging existing gradual semantics for QBAFs in these more complex frameworks. We also define a set of novel properties for gradual semantics in structured argumentation, discuss their suitability over a set of existing properties. Finally, we provide a comprehensive theoretical analysis assessing the instantiations, demonstrating the their advantages over existing gradual semantics for QBAFs and structured argumentation.

Related papers

• 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)
• A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
  We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
  arXiv  Detail & Related papers  (2023-10-18T20:18:05Z)
• Ranking-based Argumentation Semantics Applied to Logical Argumentation (full version) [2.9005223064604078]
  We investigate the behaviour of ranking-based semantics for structured argumentation. We show that a wide class of ranking-based semantics gives rise to so-called culpability measures.
  arXiv  Detail & Related papers  (2023-07-31T15:44:33Z)
• Many-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation [3.9571744700171743]
  We propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning. The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.
  arXiv  Detail & Related papers  (2022-12-14T22:10:46Z)
• Towards Preserving Semantic Structure in Argumentative Multi-Agent via Abstract Interpretation [0.0]
  We investigate the notion of abstraction from the model-checking perspective. Several arguments are trying to defend the same position from various points of view, thereby reducing the size of the argumentation framework.
  arXiv  Detail & Related papers  (2022-11-28T21:32:52Z)
• Autoregressive Structured Prediction with Language Models [73.11519625765301]
  We describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs. Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at.
  arXiv  Detail & Related papers  (2022-10-26T13:27:26Z)
• 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)
• A Diagnostic Study of Explainability Techniques for Text Classification [52.879658637466605]
  We develop a list of diagnostic properties for evaluating existing explainability techniques. We compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model's performance and the agreement of its rationales with human ones.
  arXiv  Detail & Related papers  (2020-09-25T12:01:53Z)

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.