Integrating Temporality and Causality into Acyclic Argumentation Frameworks Using a Transition System

In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language that allows us to model the evolution of the world and to establish causal relationships between the enunciation of arguments and their consequences on the acceptability of other arguments, whether direct or indirect. An Answer Set Programming implementation is also described. Furthermore, we lay the ground for the generation of explanations using the causal relations as well as two graphical representations to support such explanations.

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Publication Details

Journal
The European Journal on Artificial Intelligence
Published
2025-03-24
DOI
https://doi.org/10.1177/30504554251319451
Primary Topic
Multi-Agent Systems and Negotiation
Type
article
Field-Weighted Citation Impact
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article

Integrating Temporality and Causality into Acyclic Argumentation Frameworks Using a Transition System

Yann Munro, Gauvain Bourgne, Marie‐Jeanne Lesot, Catherine Pélachaud et al.
The European Journal on Artificial Intelligence
Multi-Agent Systems and Negotiation
article

Integrating Temporality and Causality into Acyclic Argumentation Frameworks Using a Transition System

Yann Munro, Gauvain Bourgne, Marie‐Jeanne Lesot, Catherine Pélachaud, Camilo Sarmiento, Isabelle Bloch A
article en

Abstract

In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language that allows us to model the evolution of the world and to establish causal relationships between the enunciation of arguments and their consequences on the acceptability of other arguments, whether direct or indirect. An Answer Set Programming implementation is also described. Furthermore, we lay the ground for the generation of explanations using the causal relations as well as two graphical representations to support such explanations.

The European Journal on Artificial Intelligence
Centre National de la Recherche Scientifique (FR), Université Paris Cité (FR), Sorbonne Université (FR), LIP6 (FR)
Openalex Percentile: Top 98%
Multi-Agent Systems and Negotiation
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