An Adaptive Learning Method of Personality Trait Based Mood in Mental State Transition Network by Recurrent Neural Network
This work addresses the challenge of personalized human-agent interaction in mental health or communication systems, but it appears incremental as it builds on existing concepts like MSTN and EGC.
The paper tackled the problem of enabling agents to adapt to users' personality traits for smoother communication by developing an adaptive learning method combining profit sharing and recurrent neural networks, with experimental results showing success in representing various delicate human emotions.
Mental State Transition Network (MSTN) is a basic concept of approximating to human psychological and mental responses. A stimulus calculated by Emotion Generating Calculations (EGC) method can cause the transition of mood from an emotional state to others. In this paper, the agent can interact with human to realize smooth communication by an adaptive learning method of the user's personality trait based mood. The learning method consists of the profit sharing (PS) method and the recurrent neural network (RNN). An emotion for sensor inputs to MSTN is calculated by EGC and the variance of emotion leads to the change of mental state, and then the sequence of states forms an episode. In order to learn the tendency of personality trait effectively, the ineffective rules should be removed from the episode. PS method finds out a detour in episode and should be deleted. Furthermore, RNN works to realize the variance of user's mood. Some experimental results were shown the success of representing a various human's delicate emotion.