Construction of a Dividual Model Using a Reinforcement Learning Based Bayesian Network

Abstract

A Japanese novelist Keiichiro Hirano proposes a concept of dividual to explain interaction between humans. This paper models the dividual using three machine learning techniques and develops a new human-robot interaction system using the proposed dividual model. The system consists of two parts; dividual identification and action selection. In the dividual identification, the system recognizes the person who interacts with and learns about him/her through the interaction using self-organizing map. In the action selection, the system selects an action using a Bayesian network whose conditional probability table is updated by Q-learning. Through computer simulations, it is verified that the proposed dividual model can select an appropriate dividual for the specific person to be interacted with. It is also shown that the dividual model can suggest an appropriate topic according to the specific person.