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

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.

Tourist Navigation in Android Smartphone by using Emotion Generating Calculations and Mental State Transition Networks

Mental State Transition Network which consists of mental states connected to each other is a basic concept of approximating to human psychological and mental responses. It can represent transition from an emotional state to other one with stimulus by calculating Emotion Generating Calculations method. A computer agent can transit a mental state in MSTN based on analysis of emotion by EGC method. In this paper, the Andorid EGC which the agent works in Android smartphone can evaluate the feelings in the conversation. The tourist navigation system with the proposed technique in this paper will be expected to be an emotional oriented interface in Android smartphone.