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Intelligent Questionnaires Using Approximate Dynamic Programming

Intelligent Questionnaires Using Approximate Dynamic Programming 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 q 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 q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy.This setting is quite flexible and can incorporate easily initial available data and grouped questions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png i-com de Gruyter

Intelligent Questionnaires Using Approximate Dynamic Programming

i-com , Volume 19 (3): 11 – Jan 26, 2021

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Publisher
de Gruyter
Copyright
© 2020 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2196-6826
eISSN
2196-6826
DOI
10.1515/icom-2020-0022
Publisher site
See Article on Publisher Site

Abstract

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 q 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 q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy.This setting is quite flexible and can incorporate easily initial available data and grouped questions.

Journal

i-comde Gruyter

Published: Jan 26, 2021

Keywords: Planning; Questionnaire design; Approximate dynamic programming

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