Abstract
In this paper, we propose a action generation model for entertainment robots, whose action makes them appear to be living. One problem common to entertainment robots is that it is difficult to generate a variety of actions, because robot action is generally based on simple rule-based algorithms. Thus, the simplicity of the robot's actions quickly bores users. In this paper, we propose an action generation model based on secondary needs. The secondary need indicates the needs which have a close relation to primary needs in psychology. This model uses need parameters and a neural network for choosing action. Need parameters increase gradually, and then decrease through specific action. Because the neural network learns actions that decrease the need parameters, the model begins to behave spontaneously. Simulation of
action generation shows that the model does accommodate secondary needs.
