Proposal of action selection network with estimation internal states of others

Abstract

Inferring our behavior and/or intentions is ultimate ability for autonomous interactive robots. In this paper, we propose an action selection network based multi agent planning that flexibly determines its own actions even when others intervene. The proposed method is an extension of the conventional multi -agent planning, which enables both guessing the internal states of others and planning one's own actions. We conducted an experiment using a simple block task to examine how one's own action sequence changes depending on the intervention of others. As a result, it is verified that our proposed methodology have high adaptability for unexpected reaction of users.