Learning by switching generation and reasoning methods in several knowledge representations towards the simulation of human learning process

When we solve a problem, we firstly have no knowledge and gradually acquire some piece of knowledge by observing new data, and at last arrive at complete knowledge for solving the problem. We have a simple form of specific knowledge in the first stage and a complex form of a general one in the final stage. To simulate this kind of learning mechanism, we must combine several kinds of learning methods in several stages. We proposed a method of not only reconstructing rules and switching reasoning methods in each knowledge representation but also switching rule generation methods in several knowledge representation. We simulated the method by applying to the iris classification problem