Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institutions: Université Paris Cité, Laboratoire Informatique Paris Descartes, Centre National de la Recherche Scientifique, Centre de Recherche en Informatique de Lens, École Centrale de Lyon, Université Lumière Lyon 2, Laboratoire d'Informatique en Images et Systèmes d'Information, Institut National des Sciences Appliquées de Lyon
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4400611704