Many-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation
Artificial Intelligence
2025-06-10 v1
Abstract
In this paper we propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. The approach allows for conditional reasoning over arguments and boolean combination of arguments, with respect to a class of gradual semantics, through the verification of graded (strict or defeasible) implications over a preferential interpretation. As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning in a many-valued argumentation semantics of weighted argumentation graphs. The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.
Cite
@article{arxiv.2212.07523,
title = {Many-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation},
author = {Mario Alviano and Laura Giordano and Daniele Theseider Dupré},
journal= {arXiv preprint arXiv:2212.07523},
year = {2025}
}
Comments
17 pages, 1 figure