Impression detection and management using an embodied conversational agent

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dc.contributor.authorWang, Chen
dc.contributor.authorBiancardi, Beatrice
dc.contributor.authorMancini, Maurizio
dc.contributor.authorCafaro, Angelo
dc.contributor.authorPelachaud, Catherine
dc.contributor.authorPun, Thierry
dc.contributor.authorChanel, Guillaume
dc.contributor.funderSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschungen
dc.contributor.funderAgence Nationale de la Rechercheen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2021-02-24T14:27:56Z
dc.date.available2021-02-24T14:27:56Z
dc.date.issued2020-07-10
dc.date.updated2021-02-24T14:15:36Z
dc.description.abstractEmbodied Conversational Agents (ECAs) are a promising medium for human-computer interaction, since they are capable of engaging users in real-time face-to-face interaction [1, 2]. Users’ formed impressions of an ECA (e.g. favour or dislike) could be reflected behaviourally [3, 4]. These impressions may affect the interaction and could even remain afterwards [5, 7]. Thus, when we build an ECA to impress users, it is important to detect how users feel about the ECA. The impression the ECA leaves can then be adjusted by controlling its non-verbal behaviour [7]. Motivated by the role of ECAs in interpersonal interaction and the state-of-the-art on affect recognition, we investigated three research questions: 1) which modality (facial expressions, eye movements, and physiological signals) reveals most of the formed impressions; 2) whether an ECA could leave a better impression by maximizing the impression it produces; 3) whether there are differences in impression formation during human-human vs. human-agent interaction. Our results firstly showed the interest to use different modalities to detect impressions. An ANOVA test indicated that facial expressions performance outperforms the physiological modality performance (M = 1.27, p = 0.02). Secondly, our results presented the possibility of creating an adaptive ECA. Compared with the randomly selected ECA behaviour, participants’ ratings tended to be higher in the conditions where the ECA adapted its behaviour based on the detected impressions. Thirdly, we found similar behaviour during human-human vs. human-agent interaction. People treated an ECA similarly to a human by spending more time observing the face area when forming an impression.en
dc.description.sponsorshipSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Swiss National Science Foundation (under Grant Number 2000221E-164326); Agence Nationale de la Recherche (ANR IMPRESSSIONS project number ANR-15-CE23-0023); Science Foundation Ireland (12/RC/2289_P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationWang, C., Biancardi, B., Mancini, M., Cafaro, A., Pelachaud, C., Pun, T. and Chanel, G. (2020), 'Impression Detection and Management Using an Embodied Conversational Agent'. Human-Computer Interaction. Multimodal and Natural Interaction, Lecture Notes in Computer Science book series, LNCS, vol. 12182, pp. 260-278. doi: 10.1007/978-3-030-49062-1_18en
dc.identifier.doi10.1007/978-3-030-49062-1_18en
dc.identifier.endpage278en
dc.identifier.issn0302-9743
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage260en
dc.identifier.urihttps://hdl.handle.net/10468/11104
dc.identifier.volume12182en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://link.springer.com/chapter/10.1007/978-3-030-49062-1_18
dc.rights© Springer Nature Switzerland AG 2020. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-49062-1_18en
dc.subjectAffective computingen
dc.subjectEye gazeen
dc.subjectImpression detectionen
dc.subjectImpression managementen
dc.subjectMachine learningen
dc.subjectReinforcement learningen
dc.subjectVirtual agenten
dc.titleImpression detection and management using an embodied conversational agenten
dc.typeConference itemen
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