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Intelligent Questionnaires Using Approximate Dynamic Programming

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dc.contributor.authorLogé, Frédéric
dc.contributor.authorPennec, Erwan Le
dc.contributor.authorAmadou-Boubacar, Habiboulaye
dc.date.accessioned2021-01-17T20:04:29Z
dc.date.available2021-01-17T20:04:29Z
dc.date.issued2021
dc.description.abstractInefficient interaction such as long and/or repetitive questionnaires can be detrimental to user experience, which leads us to investigate the computation of an intelligent questionnaire for a prediction task. Given time and budget constraints (maximum <em>q</em> 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.</p><p>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 <em>q</em> best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy.</p><p>This setting is quite flexible and can incorporate easily initial available data and grouped questions.en
dc.identifier.doi10.1515/icom-2020-0022
dc.identifier.pissn2196-6826
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34682
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofi-com: Vol. 19, No. 3
dc.subjectPlanning
dc.subjectQuestionnaire design
dc.subjectApproximate dynamic programming
dc.titleIntelligent Questionnaires Using Approximate Dynamic Programmingen
dc.typeText/Journal Article
gi.citation.endPage237
gi.citation.publisherPlaceBerlin
gi.citation.startPage227

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i-com Band 19 (2020) Heft 3