Konferans bildirisi Açık Erişim
Karapinar, Sertac; Sariel, Sanem
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/81867</identifier> <creators> <creator> <creatorName>Karapinar, Sertac</creatorName> <givenName>Sertac</givenName> <familyName>Karapinar</familyName> <affiliation>Istanbul Tech Univ, Artificial Intelligence & Robot Lab, Istanbul, Turkey</affiliation> </creator> <creator> <creatorName>Sariel, Sanem</creatorName> <givenName>Sanem</givenName> <familyName>Sariel</familyName> <affiliation>Istanbul Tech Univ, Artificial Intelligence & Robot Lab, Istanbul, Turkey</affiliation> </creator> </creators> <titles> <title>Cognitive Robots Learning Failure Contexts Through Experimentation</title> </titles> <publisher>Aperta</publisher> <publicationYear>2015</publicationYear> <dates> <date dateType="Issued">2015-01-01</date> </dates> <resourceType resourceTypeGeneral="Text">Conference paper</resourceType> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/81867</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.81866</relatedIdentifier> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.81867</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract">We propose a lifelong experimental learning method for cognitive robots to build and transfer knowledge among appropriate contexts. Experience gained through learning is used as a guide to future decisions of robots for both efficiency and robustness.</description> </descriptions> </resource>
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