Entropy-based Planning Algorithm for Personalized Interventions: Preliminary Report on Question-reduction Agent

Prevention of lifestyle diseases is an important issue in terms of both the lifelong well-being of each individual and the burden on society. Although several artificial-intelligence-based chatbots have been proposed to support preventing such diseases, they have not yet reached a complex problem setting that combines multiple theories of behavioral change. In such complex settings, in addition to intervention selection, it is important to reduce the amount of attributes acquired to reduce the user's burden in preventing inhibition of motivation. Therefore, we developed the Entropy-Based Planning algorithm that selects interventions from a small number of questions. To evaluate the algorithm, we conducted a short-term randomized control trial (RCT) with a small number of users as an initial validation. Due to certain limitations, we were unable to obtain results indicating sufficient effectiveness. However, important findings were obtained for the future development of behavioral-change support bots. The findings include the need to investigate the relevance of preferences among multiple behavior-change theories and balance the number of questions and accuracy of personalization on user impressions. We intend to conduct further research on the basis of these findings as well as conduct a large-scale RCT after improving the algorithm and experiment settings.