インタラクション生成に必要な情報の選択モデル

一情報の不確実性に着目したモデルの検証一

抄録

There are various kinds of information in human-human interaction, but it is difficult to process all of them. This research aims at understanding mechanism of information selection and proposes a computational model based on prediction error minimization (PEM) mechanism. The PEM mechanism is a hypothesis that explains brain function as a minimization process of prediction error. Although it has been demonstrated that action generation by the PEM mechanism is important in interactions, the situation dealing with multiple information has not been considered. This research focused on uncertainty of information and used a recurrent neural network model that learns to predict the next state of information and uncertainty. In the experiment, ball-rolling interaction between a robot with the model and that operated by an experimenter was conducted. The robot switched its action in accordance with only the information with small variance. This result suggests that uncertainty is important for information selection.