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Adaptive predictive-questionnaire by approximate dynamic-programming

dc.contributor.authorLogé, Frédéricde
dc.contributor.authorLe Pennec, Erwande
dc.contributor.authorAmadou-Boubacar, Habiboulayede
dc.contributor.editorHansen, Christiande
dc.contributor.editorNürnberger, Andreasde
dc.contributor.editorPreim, Bernhardde
dc.date.accessioned2020-08-18T15:19:47Z
dc.date.available2020-08-18T15:19:47Z
dc.date.issued2020
dc.description.abstractAs too much interaction can be detrimental to user experience, we investigate the computation of a smart questionnaire for a prediction task. Given time and budget constraints (maximum questions asked), this questionnaire will select adaptively the question sequence based on answers already given. Several use-cases with increased user and customer experience are given. The problem is framed as a Markov Decision Process and solved numerically with approximate dynamic programming, exploiting the hierarchical and episodic structure of the problem. The approach, evaluated on toy models and classic supervised learning datasets, outperforms two baselines: a decision tree with budget constraint and a model with best features systematically asked.en
dc.identifier.doi10.18420/muc2020-ws111-264
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/33506
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2020 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjectPlanning
dc.subjectQuestionnaire design
dc.subjectApproximate dynamic programming
dc.titleAdaptive predictive-questionnaire by approximate dynamic-programmingen
dc.typeText/Workshop Paper
gi.citation.publisherPlaceBonn
gi.conference.date6.-9. September 2020
gi.conference.locationMagdeburg
gi.conference.sessiontitleMCI-WS02: UCAI 2020: Workshop on User-Centered Artificial Intelligence
gi.document.qualitydigidoc

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