  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.
