A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA), based on the psychology of love, Artificial Endocrine System (AES), based on the physiology of love, and Affective State Transition (AST), based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN). The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.
Increasing Effectiveness of Model-Based Fault Diagnosis: A Dynamic Bayesian Network Design for Decision Making
