Interpretability of Gradual Semantics in Abstract Argumentation

Delobelle, Jérôme; Villata, Serena

doi:10.1007/978-3-030-29765-7_3

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Self-attacking arguments and gradual semantics in practical applications. There have already been discussions about applications where gradual (or ranking-based) semantics can be used [24,19,1]. One such application is online debates, for example, where participants propose, in the most basic form, arguments for or against a given topic or other arguments.…”

Section: Discussionmentioning

confidence: 99%

A Principle-based Account of Self-attacking Arguments in Gradual Semantics

Beuselinck¹,

Delobelle

Vesic

2023

Journal of Logic and Computation

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The issue of how a semantics should deal with self-attacking arguments was always a subject of debate among argumentation scholars. A consensus exists for extension-based semantics because those arguments are always rejected (as soon as the semantics in question respects conflict-freeness). In case of gradual semantics, the question is more complex, since other criteria are taken into account. In this paper, we check the impact of those arguments by using a principle-based approach. Principles like self-contradiction and strong self-contradiction prescribe how to deal with self-attacking arguments. We show that they are incompatible with the well-known equivalence principle (which is satisfied by almost all the existing gradual semantics), as well as with some other principles (e.g. counting). This incompatibility was not studied until now and the class of semantics satisfying self-contradiction is under-explored. In the present paper, we explore that class of semantics. We show links and incompatibilities between several principles. We define a new general oriented argumentation semantics that satisfies (strong) self-contradiction and a maximal number of compatible principles. We introduce an iterative algorithm to calculate our semantics and prove that it always converges. We also provide a characterization of our semantics. Finally, we experimentally show that our semantics is computationally efficient.

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Section: Discussionmentioning

confidence: 99%

A Principle-based Account of Self-attacking Arguments in Gradual Semantics

Beuselinck¹,

Delobelle

Vesic

2023

Journal of Logic and Computation

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“…To our knowledge, this is the first paper on explainability in quantitative bipolar argumentation. Our explanations are immediately applicable to quantitative (non-bipolar) argumentation, where explainability has not been researched either, with the exception of [5]. There, the authors formalise a notion of impact of an argument on the final strength of another argument, roughly as a difference between the final strengths of the latter argument with and without the former argument being present.…”

Section: Discussionmentioning

confidence: 99%

Explaining Change in Quantitative Bipolar Argumentation1

Kampik

Čyras

2022

Computational Models of Argument

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This paper presents a formal approach to explaining change of inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and updating the QBAF to then again draw conclusions (and so on), our approach traces changes – which we call strength inconsistencies – in the partial order that a semantics establishes on the arguments in the QBAFs. We trace the strength inconsistencies to specific arguments, which then serve as explanations. We identify both sufficient and counterfactual explanations for strength inconsistencies and show that our approach guarantees that explanation arguments exist if and only if an update leads to strength inconsistency.

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“…as defined in [10,3,32,34]. The explainability of some gradual semantics like the h-categorizer semantics [11] and the counting semantics [36] has been studied in [22]. In this paper, we focus on explaining the DF-QuAD gradual semantics [38].…”

Section: Introductionmentioning

confidence: 99%

Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks

Yin,

Potyka,

Toni

2023

Frontiers in Artificial Intelligence and Applications

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Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, explaining the quantitative reasoning outcomes of AFs under gradual semantics has not received much attention, despite widespread use in applications. In this paper, we contribute to filling this gap by proposing a novel theory of Argument Attribution Explanations (AAEs) by incorporating the spirit of feature attribution from machine learning in the context of Quantitative Bipolar Argumentation Frameworks (QBAFs): whereas feature attribution is used to determine the influence of features towards outputs of machine learning models, AAEs are used to determine the influence of arguments towards topic arguments of interest. We study desirable properties of AAEs, including some new ones and some partially adapted from the literature to our setting. To demonstrate the applicability of our AAEs in practice, we conclude by carrying out two case studies in the scenarios of fake news detection and movie recommender systems.

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Interpretability of Gradual Semantics in Abstract Argumentation

Cited by 7 publications

References 14 publications

A Principle-based Account of Self-attacking Arguments in Gradual Semantics

A Principle-based Account of Self-attacking Arguments in Gradual Semantics

Explaining Change in Quantitative Bipolar Argumentation1

Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks

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