Optimization of Fuzzy Rules in Kansei Retrieval Agent with Fuzzy Reasoning

Abstract

In this study, we propose the Kansei retrieval agent model with fuzzy reasoning and verify the optimization performance of the fuzzy rules. Kansei retrieval agent model is imitates the sensitivity of each user. Previous research has demonstrated the effectiveness in terms of presenting the user's preferences by optimizing only the membership function of fuzzy inference by numerical simulation and learning the user's evaluation criteria. However, we did not optimize the fuzzy rules of the model. Therefore, in this research, we verify the optimization performance of the Kansei retrieval agent model when optimizing the fuzzy rules using a numerical simulation. As a result, the proposed method shows to be effective from the viewpoint of user's evaluation criteria learning.