Argumentation Frameworks Features: an Initial Study

Mauro Vallati, Federico Cerutti, Massimiliano Giacomin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Semantics extensions are the outcome of the argumentation reasoning process: enumerating them is generally an intractable problem. For preferred semantics two efficient algorithms have been recently proposed, PrefSAT and SCC-P, with significant runtime variations. This preliminary work aims at investigating the reasons (argumentation framework features) for such variations. Remarkably, we observed that few features have a strong impact, and those exploited by the most performing algorithm are not the most relevant.

Original languageEnglish
Title of host publicationECAI 2014
Subtitle of host publication21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
EditorsTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
PublisherIOS Press
Pages1117-1118
Number of pages2
ISBN (Electronic)9781614994190
ISBN (Print)9781614994183
DOIs
Publication statusPublished - 1 Jan 2014
Event21st European Conference on Artificial Intelligence - Clarion Congress Hotel, Prague , Czech Republic
Duration: 18 Aug 201422 Aug 2014
Conference number: 21
https://www.ecai2014.org/programme/detailed-programme/ (Link to Conference Website)

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume263
ISSN (Print)0922-6389

Conference

Conference21st European Conference on Artificial Intelligence
Abbreviated titleECAI 2014
CountryCzech Republic
CityPrague
Period18/08/1422/08/14
Internet address

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Cite this

Vallati, M., Cerutti, F., & Giacomin, M. (2014). Argumentation Frameworks Features: an Initial Study. In T. Schaub, G. Friedrich, & B. O'Sullivan (Eds.), ECAI 2014: 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings (pp. 1117-1118). (Frontiers in Artificial Intelligence and Applications; Vol. 263). IOS Press. https://doi.org/10.3233/978-1-61499-419-0-1117