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dc.contributor.authorBroz, Frank
dc.contributor.authorNehaniv, Chrystopher
dc.contributor.authorKose-Bagci, Hatice
dc.contributor.authorDautenhahn, Kerstin
dc.date.accessioned2019-09-04T11:39:00Z
dc.date.available2019-09-04T11:39:00Z
dc.date.issued2019-06
dc.identifier.citationBroz , F , Nehaniv , C , Kose-Bagci , H & Dautenhahn , K 2019 , ' Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot ' , Philosophies , vol. 4 , no. 2 , 26 . https://doi.org/10.3390/philosophies4020026
dc.identifier.issn2409-9287
dc.identifier.urihttp://hdl.handle.net/2299/21648
dc.description.abstractIn this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.en
dc.format.extent25
dc.format.extent7017445
dc.language.isoeng
dc.relation.ispartofPhilosophies
dc.subjecthuman–robot interaction
dc.subjectturn-taking
dc.subjectdevelopmental robotics
dc.subjectcognitive architecture
dc.subjectenactive
dc.subjectexperience
dc.titleInteraction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Roboten
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.3390/philosophies4020026
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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