Adaptive Human-Robot Interaction System using Interactive EC

We created a human-robot communication system that can adapt to user preferences that can easily change through communication. Even if any learning algorithms are used, evaluating the human-robot interaction is indispensable and difficult. To solve this problem, we installed a machine learning algorithm called interactive evolutionary computation (IEC) into a communication robot named WAMOEBA-3. IEC is a kind of evolutionary computation like a genetic algorithm. With IEC, the fitness function is performed by each user. We carried out experiments on the communication learning system using an advanced IEC system named HMHE. Before the experiments, we did not tell the subjects anything about the robot, so the interaction differed among the experimental subjects. We could observe mutual adaptation, because some subjects noticed the robot´s functions and changed their interaction. From the results, we confirmed that, in spite of the changes of the preferences, the system can adapt to the interaction of multiple users