An emotion-based approach to decision making and self learning in autonomous robot control

In recent years, the fact that emotions are essential in human intelligence has been noticed. This suggests that emotions may play an important role in autonomous robot control as well. Because emotions are fuzzy in nature, this paper presents a fuzzy emotion model for an autonomous robot. Equipped with emotions, the robot is capable of evaluating the changes of both the environment and its internal states. Also, the emotion model is integrated in a decision-making/self-learning system, which is based on the associative learning strategy we presented to implement adaptive behavior control. Simulations are conducted to test the performance of the emotion model and the associative learning strategy, and the results prove the validity of them.