On the combination of argumentation solvers into parallel portfolios

Vallati, Mauro, Cerutti, Federico ORCID: https://orcid.org/0000-0003-0755-0358 and Giacomin, Massimiliano 2017. On the combination of argumentation solvers into parallel portfolios. Presented at: AI’17: The 30th Australasian Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-20 August 2017. Published in: Peng, Wei, Alahakoon, Damminda and Li, Xiaodong eds. AI 2017: Advances in Artificial Intelligence: 30th Australasian Joint Conference, Melbourne, VIC, Australia, August 19–20, 2017, Proceedings. Lecture Notes in Computer Science , vol.10400 Springer, pp. 315-327. 10.1007/978-3-319-63004-5_2

Preview

PDF - Accepted Post-Print Version Download (271kB) | Preview

Abstract

In the light of the increasing interest in efficient algorithms for solving abstract argumentation problems and the pervasive availability of multicore machines, a natural research issue is to combine existing argumentation solvers into parallel portfolios. In this work, we introduce six methodologies for the automatic configuration of parallel portfolios of argumentation solvers for enumerating the preferred extensions of a given framework. In particular, four methodologies aim at combining solvers in static portfolios, while two methodologies are designed for the dynamic configuration of parallel portfolios. Our empirical results demonstrate that the configuration of parallel portfolios is a fruitful way for exploiting multicore machines, and that the presented approaches outperform the state of the art of parallel argumentation solvers.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISBN:	9783319630038
ISSN:	0302-9743
Related URLs:	Organisation
Date of First Compliant Deposit:	11 July 2017
Date of Acceptance:	9 May 2017
Last Modified:	21 Oct 2022 07:53
URI:	https://orca.cardiff.ac.uk/id/eprint/100973

Citation Data

Cited 7 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item