Admissibility in Strength-based Argumentation: Complexity and Algorithms (Extended Version with Proofs)

• URL: http://arxiv.org/abs/2207.02258v1
• Date: Tue, 5 Jul 2022 18:42:04 GMT
• Title: Admissibility in Strength-based Argumentation: Complexity and Algorithms (Extended Version with Proofs)
• Authors: Yohann Bacquey, Jean-Guy Mailly, Pavlos Moraitis, Julien Rossit
• Abstract summary: 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.
• Score: 1.5828697880068698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Strength-based Argumentation Frameworks (StrAFs) have been proposed to model situations where some quantitative strength is associated with arguments. In this setting, the notion of accrual corresponds to sets of arguments that collectively attack an argument. Some semantics have already been defined, which are sensitive to the existence of accruals that collectively defeat their target, while their individual elements cannot. However, until now, only the surface of this framework and semantics have been studied. Indeed, the existing literature focuses on the adaptation of the stable semantics to StrAFs. In this paper, we push forward the study and investigate the adaptation of admissibility-based semantics. Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung's fundamental lemma. We therefore propose an alternative definition that induces semantics that behave as expected. We then study computational issues for these new semantics, in particular we show that complexity of reasoning is similar to the complexity of the corresponding decision problems for standard argumentation frameworks in almost all cases. We then propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions. We conclude with an experimental evaluation of our approach which shows in particular that it scales up well for solving the problem of providing one extension as well as enumerating them all.

Related papers

• Abstract Weighted Based Gradual Semantics in Argumentation Theory [3.2566808526538873]
  We introduce four important problems linking gradual semantics and acceptability degrees. First, we reexamine the inverse problem, seeking to identify the argument weights of the argumentation framework which lead to a specific final acceptability degree. Third, we ask whether argument weights can be found when preferences, rather than acceptability degrees for arguments are considered. Fourth, we consider the topology of the space of valid acceptability degrees, asking whether "gaps" exist in this space.
  arXiv  Detail & Related papers  (2024-01-21T12:22:48Z)
• 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)
• A Semantic Approach to Decidability in Epistemic Planning (Extended Version) [72.77805489645604]
  We use a novel semantic approach to achieve decidability. Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity. We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
  arXiv  Detail & Related papers  (2023-07-28T11:26:26Z)
• 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)
• Fuzzy Labeling Semantics for Quantitative Argumentation [0.0]
  We provide a novel quantitative method called fuzzy labeling for fuzzy argumentation systems. A triple of acceptability, rejectability, and undecidability degrees is used to evaluate argument strength.
  arXiv  Detail & Related papers  (2022-07-15T08:31:36Z)
• Rationale-Augmented Ensembles in Language Models [53.45015291520658]
  We reconsider rationale-augmented prompting for few-shot in-context learning. We identify rationale sampling in the output space as the key component to robustly improve performance. We demonstrate that rationale-augmented ensembles achieve more accurate and interpretable results than existing prompting approaches.
  arXiv  Detail & Related papers  (2022-07-02T06:20:57Z)
• 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)
• Deep Clustering by Semantic Contrastive Learning [67.28140787010447]
  We introduce a novel variant called Semantic Contrastive Learning (SCL) It explores the characteristics of both conventional contrastive learning and deep clustering. It can amplify the strengths of contrastive learning and deep clustering in a unified approach.
  arXiv  Detail & Related papers  (2021-03-03T20:20:48Z)
• Stability in Abstract Argumentation [2.375764121997739]
  We show how the notion of stability can be translated into reasoning with Argument-Incomplete AFs. We illustrate to what extent this notion can be useful with an application to argument-based negotiation.
  arXiv  Detail & Related papers  (2020-12-23T10:34:38Z)

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.