Bridging Personality Theory and Emotion Dynamics for Intelligent Robots: An MBTI-to-OCEAN-PAD Approach

Modeling human-like emotions in machines is a critical challenge in affective computing, particularly when considering the role of personality in shaping emotional responses. Traditional emotion models typically ignore personality differences, despite the fact that individuals with distinct traits express emotions in different ways. To address this gap, we propose a novel personality-driven machine emotion model that integrates a user-friendly personality interface based on the Myers-Briggs Type Indicator (MBTI), mapped onto the psychologically validated Big Five (OCEAN) traits, and ultimately projected into the three-dimensional Pleasure-Arousal-Dominance (PAD) emotional space. While MBTI serves as a familiar and intuitive means of user interaction in Human-Robot Interaction (HRI) contexts, OCEAN provides a robust computational backbone. We present a simulation-based validation where different personality configurations produce distinct emotion transition patterns, demonstrating alignment with known psychological theories. Our findings highlight the potential of personality-based emotion modeling to enhance the personalization and expressiveness of artificial affective systems.