Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-25) |
| Herausgeber/-innen | James Kwok |
| Seiten | 4491-4499 |
| Seitenumfang | 9 |
| ISBN (elektronisch) | 9781956792065 |
| Publikationsstatus | Veröffentlicht - 2025 |
Publikationsreihe
| Name | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| ISSN (Print) | 1045-0823 |
Abstract
Argumentation is a central subarea of Artificial Intelligence (AI) for modeling and reasoning about arguments. The semantics of abstract argumentation frameworks (AFs) is given by sets of arguments (extensions) and conditions on the relationship between them, such as stable or admissible. Today's solvers implement tasks such as finding extensions, deciding credulous or skeptical acceptance, counting, or enumerating extensions. While these tasks are well charted, the area between decision, counting/enumeration and fine-grained reasoning requires expensive reasoning so far. We introduce a novel concept (facets) for reasoning between decision and enumeration. Facets are arguments that belong to some extensions (credulous) but not to all extensions (skeptical). They are most natural when a user aims to navigate, filter, or comprehend the significance of specific arguments, according to their needs. We study the complexity and show that tasks involving facets are much easier than counting extensions. Finally, we provide an implementation, and conduct experiments to demonstrate feasibility.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-25). Hrsg. / James Kwok. 2025. S. 4491-4499 (IJCAI International Joint Conference on Artificial Intelligence).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Facets in Argumentation
T2 - A Formal Approach to Argument Significance
AU - Fichte, Johannes Klaus
AU - Fröhlich, Nicolas
AU - Hecher, Markus
AU - Lagerkvist, Victor
AU - Mahmood, Yasir
AU - Meier, Arne
AU - Persson, Jonathan
N1 - Publisher Copyright: © 2025 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2025
Y1 - 2025
N2 - Argumentation is a central subarea of Artificial Intelligence (AI) for modeling and reasoning about arguments. The semantics of abstract argumentation frameworks (AFs) is given by sets of arguments (extensions) and conditions on the relationship between them, such as stable or admissible. Today's solvers implement tasks such as finding extensions, deciding credulous or skeptical acceptance, counting, or enumerating extensions. While these tasks are well charted, the area between decision, counting/enumeration and fine-grained reasoning requires expensive reasoning so far. We introduce a novel concept (facets) for reasoning between decision and enumeration. Facets are arguments that belong to some extensions (credulous) but not to all extensions (skeptical). They are most natural when a user aims to navigate, filter, or comprehend the significance of specific arguments, according to their needs. We study the complexity and show that tasks involving facets are much easier than counting extensions. Finally, we provide an implementation, and conduct experiments to demonstrate feasibility.
AB - Argumentation is a central subarea of Artificial Intelligence (AI) for modeling and reasoning about arguments. The semantics of abstract argumentation frameworks (AFs) is given by sets of arguments (extensions) and conditions on the relationship between them, such as stable or admissible. Today's solvers implement tasks such as finding extensions, deciding credulous or skeptical acceptance, counting, or enumerating extensions. While these tasks are well charted, the area between decision, counting/enumeration and fine-grained reasoning requires expensive reasoning so far. We introduce a novel concept (facets) for reasoning between decision and enumeration. Facets are arguments that belong to some extensions (credulous) but not to all extensions (skeptical). They are most natural when a user aims to navigate, filter, or comprehend the significance of specific arguments, according to their needs. We study the complexity and show that tasks involving facets are much easier than counting extensions. Finally, we provide an implementation, and conduct experiments to demonstrate feasibility.
UR - http://www.scopus.com/inward/record.url?scp=105021818124&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2505.10982
DO - 10.48550/arXiv.2505.10982
M3 - Conference contribution
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4491
EP - 4499
BT - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-25)
A2 - Kwok, James
ER -