Abstract

Intelligent agents need to learn how the communication structure evolves within interacting groups and how to influence the groups overall behavior. We are developing methods to automatically and unobtrusively learn the social network structure that arises within a human group based on wearable sensors. Computational models of group interaction dynamics are derived from data gathered using wearable sensors. The questions we are exploring are: Can we tell who influences whom? Can we quantify this amount of influence? How can we modify group interactions to promote better information diffusion? The goal is real-time learning and modification of social network relationships by applying statistical machine learning techniques to data obtained from unobtrusive wearable sensors.