Mental State Transition Network (MSTN) is
a basic concept of approximating to human psychological and mental responses. It can represent transition from an emotional state to others by a stimulus which Emotion Generating Calculations (EGC) method calculates. In this paper, the agent using Mental State Transition Network 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). A sequence of sensor input to MSTN is
translated to an episode which consists of mental state and action. In order to learn the tendency effectively, 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 mood according to user’s personality trait. Some experimental results show the variance of human’s delicate emotion.

開催日：平成26年7月19日
会場：広島市立大学