Reasoning Method in Character Coordination System Using Fuzzy Reasoning

Abstract

In this study, we verified fuzzy inference method in the character coordination system using the Kansei retrieval agent (KaRA) model. In this verification, we conducted a comparison experiment between two methods: Min–max method, which is a commonly known inference method for fuzzy inference, and other method that uses the average value instead of the minimum value for the antecedent part membership value. The character coordination system learns user preferences by optimizing parameters of fuzzy inference in the system. Previous studies have demonstrated that the character coordination system is effective in terms of acquiring preference rules that are the evaluation criteria of users. However, in previous studies, it is not possible to extract rules that the user does not consider (Don’t care) in the evaluation of coordination. Therefore, in this study, we verify the effect of preference rules extraction when using Don’t care label which indicates that a feature not considered in the evaluation of coordination in character coordination system. As a result, we found that both methods can generate rules that fit users more than about 70%.