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dc.contributor.authorAydoğan, Reyhan
dc.contributor.authorKeskin, Onur
dc.contributor.authorÇakan, Onur
dc.date.accessioned2023-05-26T12:39:31Z
dc.date.available2023-05-26T12:39:31Z
dc.date.issued2021
dc.identifier.isbn978-981155868-9
dc.identifier.issn1860-949Xen_US
dc.identifier.urihttp://hdl.handle.net/10679/8346
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-981-15-5869-6_1
dc.description.abstractSocial robots are becoming prevalent in our society, and most of the time, they need to interact with humans in order to accomplish their tasks. Negotiation is one of the inevitable processes they need to be involved to make joint decisions with their human counterparts when there is a conflict of interest between them. This paper pursues a novel approach for a humanoid robot to negotiate with humans efficiently via speech. In this work, we propose a speech-based negotiation protocol in which agents make their offers in a turn-taking fashion via speech. We present a variant of time-based concession bidding strategy for the humanoid robot and evaluated the performance of the robot against human counterpart in human-robot negotiation experiments.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofStudies in Computational Intelligence, Part of the Studies in Computational Intelligence book series (SCI,volume 905)
dc.rightsrestrictedAccess
dc.titleLet’s negotiate with jennifer! towards a speech-based human-robot negotiationen_US
dc.typeConference paperen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-5260-9999 & YÖK ID 145578) Aydoğan, Reyhan
dc.contributor.ozuauthorAydoğan, Reyhan
dc.identifier.volume905en_US
dc.identifier.startpage3en_US
dc.identifier.endpage16en_US
dc.identifier.doi10.1007/978-981-15-5869-6_1en_US
dc.identifier.scopusSCOPUS:2-s2.0-85091282145
dc.contributor.ozugradstudentKeskin, Onur
dc.contributor.ozugradstudentÇakan, Onur
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff and Graduate Student


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