Here we propose an interactive system to augment creative design thinking using networks of concepts in a virtual reality environment. We discuss how to augment the human capacity to be creative through dynamic suggestions providing new and original ideas, based on specific semantic network characteristics. We outline directions to explore the structures of the concept network and their connection to creative concept generation. It is expected that augmented creative thinking will allow the user to have more original ideas and thus be more innovative.

In this study, we propose a method for the design of a creative and emotional motion. Our work is based on the hypothesis that a creative motion that is beyond the ordinary human imagination can produce emotional impressions that resonate with deep feeling. The proposed method involves computer implementation of motion blending, movement analogous to natural objects, rhythmic emphasis, movement of the entire design object, and sound-related effect. Using a computer system we constructed, we conducted two experiments that confirmed both our hypothesis and the effectiveness of our methodology.

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Conventional humanoid robotic behaviors are directly programmed depending on the programmer's personal experience. With this method, the behaviors usually appear unnatural. It is believed that a humanoid robot can acquire new adaptive behaviors from a human, if the robot has the criteria underlying such behaviors. The aim of this paper is to establish a method of acquiring human behavioral criteria. The advantage of acquiring behavioral criteria is that the humanoid robots can then autonomously produce behaviors for similar tasks with the same behavioral criteria but without transforming data obtained from morphologically different humans every time for every task. In this paper, a manipulator robot learns a model behavior, and another robot is created to perform the model behavior instead of being performed by a person. The model robot is presented some behavioral criteria, but the learning manipulator robot does not know them and tries to infer them. In addition, because of the difference between human and robot bodies, the body sizes of the learning robot and the model robot are also made different. The method of obtaining behavioral criteria is realized by comparing the efficiencies with which the learning robot learns the model behaviors. Results from the simulation have demonstrated that the proposed method is effective for obtaining behavioral criteria. The proposed method, the details regarding the simulation, and the results are presented in this paper.