An Exploratory Study on the Benefits of using Natural Language for Explaining Fuzzy Rule-based Systems

This paper presents an empirical research. It focuses on testing empirically the benefits of providing users, in a specific domain, with textual interpretation of the fuzzy inferences carried out by a fuzzy classifier for a given selection of samples. The hypothesis to test is as follows: "Users understand easier the decision made by a fuzzy system when they are provided with a textual interpretation of the fuzzy inference mechanism that the system carried out". This hypothesis was successfully tested in a web survey. The application domain was leaf classification. The fuzzy classifiers were built with the GUAJE fuzzy modeling open source software which is aimed at generating interpretable fuzzy systems. The textual interpretation was handmade by an expert who followed the guidelines of the Natural Language Generation approach proposed by Reiter and Dale. Reported results encourage us to go on with a series of additional experiments devoted to deeply explore how Natural Language Generation techniques can contribute to facilitate the understanding of fuzzy systems.