Integrated Adaptive Resonance Theory Neural Model for Episodic Memory with Task Memory for Task Performance of Robots

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dc.contributor.authorPark, Ju-Younko
dc.contributor.authorYoo, Yong-Hoko
dc.contributor.authorKim, Deok-Hwako
dc.contributor.authorKim, Jong-Hwanko
dc.date.accessioned2017-01-03T05:34:15Z-
dc.date.available2017-01-03T05:34:15Z-
dc.date.created2016-11-21-
dc.date.created2016-11-21-
dc.date.issued2016-07-28-
dc.identifier.citation2016 IEEE World Congress on Computational Intelligence , pp.4873 - 4880-
dc.identifier.urihttp://hdl.handle.net/10203/215211-
dc.description.abstractEpisodic memory is the memory of personal experiences as episodes with subjective time. Task memory is defined as a memory for storing the knowledge of sequential procedures to perform tasks. Rather than encoding and retrieving such a temporal sequence of events or procedures, respectively, it is more efficient to implement both memories into a single memory model together. For this purpose, this paper proposes an integrated adaptive resonance theory (I-ART) neural model for episodic memory with task memory. The performance of the proposed episodic memory model is confirmed through comparison study with the other methods. And the proposed task memory is applied to perform tasks by Mybot-KSR2, developed in RIT Lab., KAIST.-
dc.languageEnglish-
dc.publisherIEEE Computational Intelligence Society (IEEE CIS)-
dc.titleIntegrated Adaptive Resonance Theory Neural Model for Episodic Memory with Task Memory for Task Performance of Robots-
dc.typeConference-
dc.identifier.wosid000390749105009-
dc.identifier.scopusid2-s2.0-85008249207-
dc.type.rimsCONF-
dc.citation.beginningpage4873-
dc.citation.endingpage4880-
dc.citation.publicationname2016 IEEE World Congress on Computational Intelligence-
dc.identifier.conferencecountryCN-
dc.identifier.conferencelocationVancouver Convention Centre-
dc.identifier.doi10.1109/CEC.2016.7744415-
dc.contributor.localauthorKim, Jong-Hwan-
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EE-Conference Papers(학술회의논문)
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