Action generation model based on changes in state patterns

In this paper, we propose a self-organized learning model that can generate behaviors for successfully performing various tasks. The model memorizes various relationships between changes in a state pattern and a motor command through learning. After the learning, the model can perform various tasks by generating the various behaviors automatically. We confirmed the performance of the model by applying it to a mobile robot simulation. The results indicate that suitable behaviors for all the tasks generated spontaneously. Additionally, we propose a sequential learning method which modifies the memorized various relationships while the model executes the task. And we confirmed the effectiveness of the sequential learning by the simulation.