Argumentation for machine learning: a survey
File(s)
Published version
Author(s)
Cocarascu, O
Toni, F
Type
Conference Paper
Abstract
Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future.
Editor(s)
Baroni, P
Gordon, TF
Scheffler, T
Stede, M
Date Issued
2016-09-16
Date Acceptance
2016-09-12
Citation
Computational Models of Argument, 2016, 287, pp.219-230
ISBN
978-1-61499-686-6
ISSN
0922-6389
Publisher
IOS PRESS
Start Page
219
End Page
230
Journal / Book Title
Computational Models of Argument
Volume
287
Copyright Statement
© 2016 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000383377900023&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
6th International Conference on Computational Models of Argument (COMMA)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Logic
Computer Science
Science & Technology - Other Topics
Argumentation
Machine Learning
Publication Status
Published
Start Date
2016-09-12
Finish Date
2016-09-16
Coverage Spatial
Univ Potsdam, Potsdam, GERMANY