A Software Framework to Encode the Psychological Dimensions of an Artificial Agent

Robotic personalities broaden the social dimension of an agent creating feelings of comfort in humans. In this work, we propose a taxonomy model to generate synthetic personalities based on the Big Five model. In particular, this paper describes a generalized framework for artificial personalities whose core is a Bidirectional Encoder Representations from Transformers (BERT) model capable of associating behaviors tailored to each personality trait. The generator is fully integrated within a modular software architecture capable of performing social interaction tasks, being at the same time task- and platformindependent. The proposed framework has been tested in a pilot experiment where human subjects were asked to interact with a humanoid robot displaying different personality traits. Results obtained by the statistical analysis of validated questionnaires show interesting insights about the capability of the framework of generating personalities that are clearly perceived by users, and whose personality dimensions are strongly distinguishable. Index Terms—Artificial personality; Human-Robot Interaction; Personality-adaptive architecture; Social Robotics