Adaptive predictive-questionnaire by approximate dynamic-programming

Abstract

As 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.