Towards an emotion core based on a hidden Markov model

Abstract

An emotion core for autonomous robots based on a hidden Markov model is proposed. Different emotional robot characters can be designed by tuning state transition probabilities. Perception of stimuli has an impact on emotional state transitions, and, thus, affects emotion dynamics and observable expressions/actions. This work proposes the methodology of design and implementation, and shows integration into a decision and control architecture. The application potential in an emotion-based human-robot interaction is discussed.