Behavior development through interaction between acquisition and recognition of observed behaviors

Abstract

Life-time development of behavior learning seems based on not only self-learning architecture but also explicit/implicit teaching from other agents that is expected to accelerates the learning. This paper presents a method for a robot to understand unfamiliar behaviors shown by others through the collaboration between behavior acquisition and recognition of observed behaviors, where the state value has an important role not simply for behavior acquisition (reinforcement learning) but also for behavior recognition (observation). That is, the state value updates can be accelerated by observation without real trials and errors while the learned values enrich the recognition system since it is based on estimation of the state value of the observed behavior. The validity of the proposed method is shown by applying it to a dynamic environment where two robots play soccer.