Abstract
Both self-learning architecture and explicit/implicit teaching from other agents are necessary not only for learning beahvior for a task but more seriously for life-time behavior learning. This paper presents a method for a robot to understand unfamiliar behavior shown by others through the interaction between behavior acquisition and recognition of observed behavior, 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 trial and error 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.
