Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks
This work addresses the challenge of elucidating emotion mechanisms for applications in AI and robotics, though it appears incremental as it builds on existing computational models without specifying major breakthroughs.
The authors tackled the problem of modeling human emotions computationally to advance understanding of natural intelligence and enable empathetic partner robots, proposing a deep neural network model with three interacting modules that exhibited reasonable behavior in simulations.
Emotions are very important for human intelligence. For example, emotions are closely related to the appraisal of the internal bodily state and external stimuli. This helps us to respond quickly to the environment. Another important perspective in human intelligence is the role of emotions in decision-making. Moreover, the social aspect of emotions is also very important. Therefore, if the mechanism of emotions were elucidated, we could advance toward the essential understanding of our natural intelligence. In this study, a model of emotions is proposed to elucidate the mechanism of emotions through the computational model. Furthermore, from the viewpoint of partner robots, the model of emotions may help us to build robots that can have empathy for humans. To understand and sympathize with people's feelings, the robots need to have their own emotions. This may allow robots to be accepted in human society. The proposed model is implemented using deep neural networks consisting of three modules, which interact with each other. Simulation results reveal that the proposed model exhibits reasonable behavior as the basic mechanism of emotion.