Extension-ranking Semantics for Abstract Argumentation Preprint
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910923204067328 |
|---|---|
| author | Skiba, Kenneth Rienstra, Tjitze Thimm, Matthias Heyninck, Jesse Kern-Isberner, Gabriele |
| author_facet | Skiba, Kenneth Rienstra, Tjitze Thimm, Matthias Heyninck, Jesse Kern-Isberner, Gabriele |
| contents | In this paper, we present a general framework for ranking sets of arguments in abstract argumentation based on their plausibility of acceptance. We present a generalisation of Dung's extension semantics as extension-ranking semantics, which induce a preorder over the power set of all arguments, allowing us to state that one set is "closer" to being acceptable than another. To evaluate the extension-ranking semantics, we introduce a number of principles that a well-behaved extension-ranking semantics should satisfy. We consider several simple base relations, each of which models a single central aspect of argumentative reasoning. The combination of these base relations provides us with a family of extension-ranking semantics. We also adapt a number of approaches from the literature for ranking extensions to be usable in the context of extension-ranking semantics, and evaluate their behaviour. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extension-ranking Semantics for Abstract Argumentation Preprint Skiba, Kenneth Rienstra, Tjitze Thimm, Matthias Heyninck, Jesse Kern-Isberner, Gabriele Artificial Intelligence In this paper, we present a general framework for ranking sets of arguments in abstract argumentation based on their plausibility of acceptance. We present a generalisation of Dung's extension semantics as extension-ranking semantics, which induce a preorder over the power set of all arguments, allowing us to state that one set is "closer" to being acceptable than another. To evaluate the extension-ranking semantics, we introduce a number of principles that a well-behaved extension-ranking semantics should satisfy. We consider several simple base relations, each of which models a single central aspect of argumentative reasoning. The combination of these base relations provides us with a family of extension-ranking semantics. We also adapt a number of approaches from the literature for ranking extensions to be usable in the context of extension-ranking semantics, and evaluate their behaviour. |
| title | Extension-ranking Semantics for Abstract Argumentation Preprint |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.21683 |