ABSTRACT
Emotionally intelligent robots can recognize, interpret, and respond to human emotions by utilizing machine learning, natural language processing, and robot vision to analyze facial expressions, voice, and other nonverbal cues. The main components of an emotional robot algorithm are outlined, highlighting the key considerations and concerns that must be addressed during algorithm formulation. Specific algorithms used in emotionally intelligent robots are discussed, namely Bayesian inference and fuzzy logic. Bayesian inference offers a probabilistic approach to emotion recognition. Fuzzy logic is a platform to handle the ambiguous nature of human emotional behavior by interpreting uncertain or vague information about emotions based on a range of input stimuli.
