Intelligent Questionnaires Using Approximate Dynamic Programming - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue i-com Année : 2021

Intelligent Questionnaires Using Approximate Dynamic Programming

Résumé

Abstract Inefficient 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.
Fichier non déposé

Dates et versions

hal-04361964 , version 1 (22-12-2023)

Identifiants

Citer

Frédéric Logé, Erwan Le Pennec, Habiboulaye Amadou Boubacar. Intelligent Questionnaires Using Approximate Dynamic Programming. i-com, 2021, 19 (3), pp.227-237. ⟨10.1515/icom-2020-0022⟩. ⟨hal-04361964⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More