An Adaptive Learning Method of Personality Trait Based Mood in Mental State Transition Network by Recurrent Neural Network

TL;DR

This paper presents an adaptive learning approach combining profit sharing and recurrent neural networks to model and predict human mood transitions based on personality traits, enhancing human-agent interaction.

Contribution

It introduces a novel method integrating profit sharing and RNN to learn and adapt to individual personality-driven mood changes in MSTN models.

Findings

Successfully models delicate human emotions

Effectively learns personality trait tendencies

Improves human-agent communication

Abstract

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.