Virtual Assistant supported by Fusion of Fuzzy Rule-based Explainers

In this work, we introduce a novel methodology for combining different explanatory inputs into a singular coherent narrative in the medical domain. As proof of concept, we have implemented a prototype ready to diagnose diabetes and heart disease. The proposal is validated with a user study in which we collected 48 valid responses to a given questionnaire. We compared four different strategies for explanation fusion, considering explanations coming from two different fuzzy rule bases. The winner strategy (i.e., verbalizing common antecedents as the main factors of the diagnosis and adding any non common antecedents as supporting evidence) was perceived as trustworthy, useful, and natural by most participants in the study.

keywords: Explainable Fuzzy Systems, Natural Language Generation, Trustworthy Conversational Agents, Human evaluation