An Extended Bayesian User Model (BUM) for Capturing Cultural Attributes with a Social Robot

Abstract

In this work we propose a Bayesian User Model which is able capture a unified representation of cultural attributes from heterogeneous information in the context of Human-Robot Interaction. Despite the latest advances in robotic technologies, virtually no robots are able to cope with the specificities of the “modus vivendi” of different cultures. We start by proposing Bayesian classifiers to capture unitary attributes of different users, clustering them in a n-dimensional semantic attribute space, aggregating groups of persons that share similar attributes. Results show a highly accurate classification framework, both capable of detecting specific subtleties in user's properties, and generalizing them into representative profiles. We then discuss its application towards adapting the actions of a robot and its potential impact on culture-awareness, demonstrating how the proposed framework can enable culture-awareness, exploring this new frontier in social robotics.