A Game Playing Robot That Can Learn A Tactical Knowledge Through Interacting With A Human

We propose a new approach for teaching a humanoid-robot a task online without pre-set data provided in advance. In our approach, human acts as a collaborator and also as a teacher. The proposed approach enables the humanoid-robot to learn a task through multi-component interactive architecture. The components are designed with the respect to human methodology for learning a task through empirical interactions. For efficient performance, the components are isolated within one single API. Our approach can be divided into five main roles: perception, representation, state/knowledge-up-dating, decision making and expression. A conducted empirical experiment for the proposed approach is to be done by teaching a Fujitsu's humanoid-robot "Hoap-3" an X-O game strategy and its results are to be done and explained. Important component such as observation, structured interview, knowledge integration and decision making are described for teaching the robot the game strategy while conducting the experiment.