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dc.contributor.authorFischer, K.
dc.contributor.authorSaunders, J.
dc.contributor.authorLehmann, H.
dc.contributor.authorNehaniv, C.L.
dc.contributor.authorLohan, K.S.
dc.contributor.authorPitsch, K.
dc.contributor.authorRohlfing, K.J.
dc.contributor.authorWrede, B.
dc.date.accessioned2012-03-26T17:00:40Z
dc.date.available2012-03-26T17:00:40Z
dc.date.issued2011-01-01
dc.identifier.citationFischer , K , Saunders , J , Lehmann , H , Nehaniv , C L , Lohan , K S , Pitsch , K , Rohlfing , K J & Wrede , B 2011 , Contingency allows the robot to spot the tutor and to learn from interaction . in Procs of 2011 IEEE Int Conf on Development and Learning : ICDL 2011 . IEEE , 2011 IEEE Int Conf on Development and Learning, ICDL , Rome , Italy , 24/08/11 . https://doi.org/10.1109/DEVLRN.2011.6037341
dc.identifier.citationconference
dc.identifier.isbn978-1-61284-989-8
dc.identifier.otherPURE: 466353
dc.identifier.otherPURE UUID: 80a7625c-35d0-45fb-991d-71c94d8c0138
dc.identifier.otherScopus: 80054972259
dc.identifier.urihttp://hdl.handle.net/2299/8050
dc.description.abstractAiming at artificial system learning from a human tutor elicit tutoring behavior, which we implemented on the robotic platform iCub. For the evaluation of the system with users, we considered a contingency module that is developed to elicit tutoring behavior, which we then evaluate by implementing this module on the robotic platform iCub and within an interaction with the users. For the evaluation of our system, we consider not only the participant's behavior but also the system's log-files as dependent variables (as it was suggested in [15] for the improvement of HRI design). We further applied Sequential Analysis as a qualitative method that provides micro-analytical insights into the sequential structure of the interaction. This way, we are able to investigate a closer interrelationship between robot's and tutor's actions and how they respond to each other. We focus on two cases: In the first case, the system module was reacting to the interaction partner appropriately; in the second case, the contingency module failed to spot the tutor. We found that the contingency module enables the robot to engage in an interaction with the human tutor who orients to the robot's conduct as appropriate and responsive. In contrast, when the robot did not engage in an appropriate responsive interaction, the tutor oriented more towards the object while gazing less at the robot.en
dc.format.extent8
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs of 2011 IEEE Int Conf on Development and Learning
dc.titleContingency allows the robot to spot the tutor and to learn from interactionen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionAdaptive Systems
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=80054972259&partnerID=8YFLogxK
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted2011-01-01
rioxxterms.versionofrecordhttps://doi.org/10.1109/DEVLRN.2011.6037341
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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