Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks

TL;DR

This paper proposes a deep neural network-based computational model of emotion that aims to elucidate emotional mechanisms and facilitate empathetic robots, with simulation results demonstrating its basic emotional behavior.

Contribution

It introduces a novel deep neural network model of emotion with three interacting modules, advancing understanding of emotional mechanisms in AI.

Findings

Model exhibits reasonable emotional behavior in simulations

Three-module neural network effectively simulates emotional interactions

Potential for empathetic robots in human society

Abstract

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.