BUM: Bayesian user model for distributed social robots

Abstract

In this work we present a Bayesian User Model for inferring the characteristics and inter-user patterns of a population users. The model can receive evidence gathered by various interactive devices, such as social robots or wearable devices. The system is modular, with each module being responsible for gathering information and observations from persons present in the system's operation scenario. This information enables each module to determine a single characteristic of the person. New observations and measurements received by the system are fused with previous knowledge by a sub-process based on an information theory technique. This allows the system to be implemented in diverse heterogeneous distributed system topologies, extending beyond robotics. We have conducted experiments involving a team of social robots and simulated user population. Our experiments have shown that the system is able to learn and classify the persons' characteristics, and to find relevant user groups via clustering. This system can potentially be used to gather information on a large set of persons, as well as to be an information source for user-adaptive applications in areas such as Robotics, Ambient Assisted Living (AAL) and Internet of Things.