Inference Solutions for Natural Language Propositions Involving Fuzzy Quantifiers and Truth Qualifiers by Simulation using Random Numbers

Abstract

In this paper, we get inference solutions for natural language propositions involving fuzzy quantifiers and truth qualifiers numerically by simulation using random numbers. We apply shift type transformation for fuzzy subjects. We find the inference solutions depend on high value number h. The h gets smaller, the tumor of the solutions (total displacement from y=x line) becomes larger. We use both straight line subjects and S-function subjects. We find no difference between the solutions in straight line subjects and those in S-function subjects.