A SCC recursive meta-algorithm for computing preferred labellings in abstract argumentation

Cerutti, Federico ORCID: https://orcid.org/0000-0003-0755-0358, Giacomin, Massimiliano, Vallati, Mauro and Zanella, Marina 2014. A SCC recursive meta-algorithm for computing preferred labellings in abstract argumentation. Presented at: 14th International conference on Principles of Knowledge Representation and Reasoning (KR-2014), Vienna, Austria, 20-24 July 2014. Proceedings of the Fourteenth International Conference on Principles of Knowledge Representation and Reasoning. AAAI,

Preview

PDF - Accepted Post-Print Version Download (290kB) | Preview

Abstract

This paper presents a meta-algorithm for the computation of preferred labellings, based on the general recursive schema for argumentation semantics called SCC-Recursiveness. The idea is to recursively decompose a framework so as to compute semantics labellings on restricted sub-frameworks, in order to reduce the computational effort. The meta-algorithm can be instantiated with a specific “base algorithm”, applied to the base case of the recursion, which can be obtained by generalizing existing algorithms in order to compute labellings in restricted sub-frameworks. We devise for this purpose a generalization of a SAT-based algorithm, and provide an empirical investigation to show the significant improvement of performances obtained by exploiting the SCC-recursive schema.

Item Type:	Conference or Workshop Item (Speech)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information:	The contents of this journal will be available in an open access format 15 month(s) after an issue is published (http://www.aaai.org/ojs/index.php/aimagazine/about/editorialPolicies).
Publisher:	AAAI
ISBN:	9781577356578
Date of First Compliant Deposit:	12 May 2016
Date of Acceptance:	1 January 2014
Last Modified:	01 Nov 2022 09:53
URI:	https://orca.cardiff.ac.uk/id/eprint/89579

Citation Data

Cited 22 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item