Argument Ranking with Categoriser Function

Pu, Fuan; Luo, Jian; Zhang, Yulai; Luo, Guangsheng

doi:10.1007/978-3-319-12096-6_26

Cited by 25 publications

(28 citation statements)

References 11 publications

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“…otherwise [17] defines a ranking from the categoriser function, when the argumentation framework is a tree. Pu et al [62] have proved that this semantics always yields a (unique) result for any argumentation framework. The categoriser ranking-based semantics associates to any argumentation framework F = A, R a ranking Cat F on A such that, ∀a, b ∈ A:…”

Section: Definition 4 ([26]mentioning

confidence: 94%

Constraints and changes: A survey of abstract argumentation dynamics

Doutre

Mailly

2018

AAC

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This paper addresses the issue of the dynamic enforcement of a constraint in an argumentation system. The system consists in (1) an argumentation framework, made up, notably, of a set of arguments and of an attack relation, (2) an evaluation semantics, and (3) the evaluation result, computed from (1) and (2). An agent may want another agent to consider a new attack, or to have a given argument accepted, or even to relax the definition of the semantics. A constraint on any of the three components is thus defined, and it has to be enforced in the system. The enforcement may result in changes on components of the system. The paper surveys existing approaches for the dynamic enforcement of a constraint and its consequences, and reveals challenging enforcement cases that remain to be investigated.

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Section: Definition 4 ([26]mentioning

confidence: 94%

Constraints and changes: A survey of abstract argumentation dynamics

Doutre

Mailly

2018

AAC

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“…h-categorizer semantics [5,15] This gradual semantics uses a categorizer function to assign a value to each argument which captures the relative strength of an argument taking into account the strength of its attackers, which itself takes into account the strength of its attackers, and so on.…”

Section: Definition 3 (Gradual Semantics) a Gradual Semantics Is A Fmentioning

confidence: 99%

Interpretability of Gradual Semantics in Abstract Argumentation

Delobelle

Villata

2019

Lecture Notes in Computer Science

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Argumentation, in the field of Artificial Intelligence, is a formalism allowing to reason with contradictory information as well as to model an exchange of arguments between one or several agents. For this purpose, many semantics have been defined with, amongst them, gradual semantics aiming to assign an acceptability degree to each argument. Although the number of these semantics continues to increase, there is currently no method allowing to explain the results returned by these semantics. In this paper, we study the interpretability of these semantics by measuring, for each argument, the impact of the other arguments on its acceptability degree. We define a new property and show that the score of an argument returned by a gradual semantics which satisfies this property can also be computed by aggregating the impact of the other arguments on it. This result allows to provide, for each argument in an argumentation framework, a ranking between arguments from the most to the least impacting ones w.r.t. a given gradual semantics.

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“…For comparison purpose, we also include in this table the results of some semantics from the literature where the same set [5] of properties has been already checked. Namely, these semantics are: the semantics based on Social Argumentation Frameworks SAF [16,11,8] restricted to Dung's argumentation framework, the semantics Categoriser Cat [3,19], the Discussion-based semantics Dbs and the Burden-based semantics Bbs [1], the global semantics based on tuple values T uples * [7], and the semantics M&T [17]. Table 2.…”

Section: Other Propertiesmentioning

confidence: 99%

“…Taken in their abstract form, such networks are argumentation frameworks, as defined by Dung [10]. Sharing the view that identifying sets of mutually acceptable arguments (extensions) is sometimes not sufficient, many "gradual" (returning a value) [3,17,9,16] or "ranking" (returning an order) semantics have been proposed [7,1,19,13,20,2,6]. Each of these proposals has some merit, and nicely designed examples convince indeed that, in some situations at least, they should be the method of choice.…”

Section: Introductionmentioning

confidence: 99%

A Parametrized Ranking-Based Semantics for Persuasion

Bonzon

Delobelle

Konieczny

et al. 2017

Lecture Notes in Computer Science

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In this paper we question the ability of the existant ranking semantics for argumentation to capture persuasion settings, emphasizing in particular the phenomena of protocatalepsis (the fact that it is often efficient to anticipate the counter-arguments of the audience), and of fading (the fact that long lines of argumentation become ineffective). It turns out that some widely accepted principles of ranking-based semantics are incompatible with a faithful treatment of these phenomena. We thus propose a parametrized semantics based on propagation of values, which allows to control the scope of arguments to be considered for evaluation. We investigate its properties (identifying in particular threshold values guaranteeing that some properties hold), and report experimental results showing that the family of rankings that may be returned have a high coherence rate.

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Argument Ranking with Categoriser Function

Cited by 25 publications

References 11 publications

Constraints and changes: A survey of abstract argumentation dynamics

Constraints and changes: A survey of abstract argumentation dynamics

Interpretability of Gradual Semantics in Abstract Argumentation

A Parametrized Ranking-Based Semantics for Persuasion

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