Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 805–834 |
| Seitenumfang | 30 |
| Fachzeitschrift | Journal of Artificial Intelligence Research |
| Jahrgang | 80 |
| Publikationsstatus | Veröffentlicht - 23 Juni 2024 |
Abstract
In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics, including credulous reasoning. When asking for projected counts, we are interested in counting the number of extensions of a given argumentation framework, while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by the treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming. Our algorithms run in double or triple exponential time in the treewidth, depending on the semantics under consideration. Finally, we establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension under the exponential time hypothesis (ETH).
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
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in: Journal of Artificial Intelligence Research, Jahrgang 80, 23.06.2024, S. 805–834.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Counting Complexity for Reasoning in Abstract Argumentation
AU - Fichte, Johannes Klaus
AU - Hecher, Markus
AU - Meier, Arne
N1 - Publisher Copyright: ©2024 The Authors.
PY - 2024/6/23
Y1 - 2024/6/23
N2 - In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics, including credulous reasoning. When asking for projected counts, we are interested in counting the number of extensions of a given argumentation framework, while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by the treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming. Our algorithms run in double or triple exponential time in the treewidth, depending on the semantics under consideration. Finally, we establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension under the exponential time hypothesis (ETH).
AB - In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics, including credulous reasoning. When asking for projected counts, we are interested in counting the number of extensions of a given argumentation framework, while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by the treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming. Our algorithms run in double or triple exponential time in the treewidth, depending on the semantics under consideration. Finally, we establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension under the exponential time hypothesis (ETH).
UR - http://www.scopus.com/inward/record.url?scp=85197347726&partnerID=8YFLogxK
U2 - 10.1613/jair.1.16210
DO - 10.1613/jair.1.16210
M3 - Article
VL - 80
SP - 805
EP - 834
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
SN - 1076-9757
ER -