Impact Measures for Gradual Argumentation Semantics

Fuente: arXiv
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Autores principales: Anaissy, Caren Al, Delobelle, Jérôme, Vesic, Srdjan, Yun, Bruno
Formato: Preprint
Publicado: 2024
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author Anaissy, Caren Al
Delobelle, Jérôme
Vesic, Srdjan
Yun, Bruno
author_facet Anaissy, Caren Al
Delobelle, Jérôme
Vesic, Srdjan
Yun, Bruno
contents Argumentation is a formalism allowing to reason with contradictory information by modeling arguments and their interactions. There are now an increasing number of gradual semantics and impact measures that have emerged to facilitate the interpretation of their outcomes. An impact measure assesses, for each argument, the impact of other arguments on its score. In this paper, we refine an existing impact measure from Delobelle and Villata and introduce a new impact measure rooted in Shapley values. We introduce several principles to evaluate those two impact measures w.r.t. some well-known gradual semantics. This comprehensive analysis provides deeper insights into their functionality and desirability.
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id arxiv_https___arxiv_org_abs_2407_08302
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Impact Measures for Gradual Argumentation Semantics
Anaissy, Caren Al
Delobelle, Jérôme
Vesic, Srdjan
Yun, Bruno
Artificial Intelligence
Argumentation is a formalism allowing to reason with contradictory information by modeling arguments and their interactions. There are now an increasing number of gradual semantics and impact measures that have emerged to facilitate the interpretation of their outcomes. An impact measure assesses, for each argument, the impact of other arguments on its score. In this paper, we refine an existing impact measure from Delobelle and Villata and introduce a new impact measure rooted in Shapley values. We introduce several principles to evaluate those two impact measures w.r.t. some well-known gradual semantics. This comprehensive analysis provides deeper insights into their functionality and desirability.
title Impact Measures for Gradual Argumentation Semantics
topic Artificial Intelligence
url https://arxiv.org/abs/2407.08302