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Article: A multi-layered learning model

TitleA multi-layered learning model
Authors
KeywordsBlackboard Systems
Concurrent Learning
Machine Learning
Meta-Learning
Opportunistic Learning
Issue Date1993
PublisherSpringer Verlag Dordrecht. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0921-0296
Citation
Journal Of Intelligent & Robotic Systems, 1993, v. 8 n. 3, p. 399-423 How to Cite?
AbstractThe growth of technology is leading mankind to an increased awareness of the need for more intelligent systems. However, one of the bottlenecks in building intelligent systems is the difficulty of acquisition, testing and refinement of domain specialists' knowledge. Learning capability offers a way through this bottleneck. In this paper, we describe a general-purpose learning model for use in an unstructured environment. The proposed model exploits different learning techniques to improve the coordination, to increase task and resource allocation efficiency and to refine problem-solving skills of system elements. The utility of such system is most evident in complex domains such as 'grasping unknown objects by a dextrous hand'. An example of the proposed model is illustrated by an intelligent dextrous hand which learns to grasp unknown objects. Moreover, an expert system for grasp mode selection was implemented in a software package and an example of grasp mode generation is demonstrated. © 1993 Kluwer Academic Publishers.
Persistent Identifierhttp://hdl.handle.net/10722/154990
ISSN
2015 Impact Factor: 0.932
2015 SCImago Journal Rankings: 0.629
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJanabiSharifi, Fen_US
dc.contributor.authorWilson, WJen_US
dc.contributor.authorPang, GKHen_US
dc.date.accessioned2012-08-08T08:31:25Z-
dc.date.available2012-08-08T08:31:25Z-
dc.date.issued1993en_US
dc.identifier.citationJournal Of Intelligent & Robotic Systems, 1993, v. 8 n. 3, p. 399-423en_US
dc.identifier.issn0921-0296en_US
dc.identifier.urihttp://hdl.handle.net/10722/154990-
dc.description.abstractThe growth of technology is leading mankind to an increased awareness of the need for more intelligent systems. However, one of the bottlenecks in building intelligent systems is the difficulty of acquisition, testing and refinement of domain specialists' knowledge. Learning capability offers a way through this bottleneck. In this paper, we describe a general-purpose learning model for use in an unstructured environment. The proposed model exploits different learning techniques to improve the coordination, to increase task and resource allocation efficiency and to refine problem-solving skills of system elements. The utility of such system is most evident in complex domains such as 'grasping unknown objects by a dextrous hand'. An example of the proposed model is illustrated by an intelligent dextrous hand which learns to grasp unknown objects. Moreover, an expert system for grasp mode selection was implemented in a software package and an example of grasp mode generation is demonstrated. © 1993 Kluwer Academic Publishers.en_US
dc.languageengen_US
dc.publisherSpringer Verlag Dordrecht. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0921-0296en_US
dc.relation.ispartofJournal of Intelligent & Robotic Systemsen_US
dc.subjectBlackboard Systemsen_US
dc.subjectConcurrent Learningen_US
dc.subjectMachine Learningen_US
dc.subjectMeta-Learningen_US
dc.subjectOpportunistic Learningen_US
dc.titleA multi-layered learning modelen_US
dc.typeArticleen_US
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_US
dc.identifier.authorityPang, GKH=rp00162en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/BF01257951en_US
dc.identifier.scopuseid_2-s2.0-0027809411en_US
dc.identifier.volume8en_US
dc.identifier.issue3en_US
dc.identifier.spage399en_US
dc.identifier.epage423en_US
dc.identifier.isiWOS:A1993MP83600006-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridJanabiSharifi, F=7004092090en_US
dc.identifier.scopusauthoridWilson, WJ=7404031503en_US
dc.identifier.scopusauthoridPang, GKH=7103393283en_US

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