Hybrid Multiagent System for Automatic Object Learning Classification

Gil González, Ana Belén; Prieta Pintado, Fernando de la; López Batista, Vivian Félix

Título

dc.contributor.author	Gil González, Ana Belén
dc.contributor.author	Prieta Pintado, Fernando de la
dc.contributor.author	López Batista, Vivian Félix
dc.date.accessioned	2017-09-06T09:14:50Z
dc.date.available	2017-09-06T09:14:50Z
dc.date.issued	2010-06
dc.identifier.citation	Hybrid Artificial Intelligence Systems Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 6077, pp. 61-68.
dc.identifier.isbn	978-3-642-13802-7 (Print) / 978-3-642-13803-4 (Online)
dc.identifier.issn	0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.uri	http://hdl.handle.net/10366/134919
dc.description.abstract	The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories. This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.publisher	Springer Science + Business Media
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	Hybrid Multiagent System for Automatic Object Learning Classification
dc.type	info:eu-repo/semantics/conferenceObject
dc.rights.accessRights	info:eu-repo/semantics/openAccess