Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Humans do not always learn using the same knowledge representation. They use concrete knowledge representation at first, but they gradually come to use abstract one as they learn more. We have studied a system that switches knowledge representation, reconstructs knowledge set and switches reasoning method. The system does them as soon as conditions are satisfied.In this paper, we extend the system to switch knowledge representation smoothly. The extended system reasons by using not only a new knowledge representation but also an old one depending on the ratio of swtching. We take the cases into account where the system starts to switch knowledge representation under switching reasoning method. As a result of learnig for some kinds of data, the system shows a different result from the old one. In particular, the rate of correct answer increases for complex data by decreasing incorrect answers with a lack of knowledge.
