Planning by case-based reasoning based on fuzzy logic

TL;DR

This paper introduces Fuzzy-BML, a fuzzy logic-based approach for case-based reasoning that models retrieval processes using fuzzy rules and linguistic variables, enhancing handling of imprecise information in complex systems.

Contribution

It presents a novel Boolean modeling method for fuzzy reasoning in CBR, integrating fuzzy logic with induction graph classification for improved retrieval.

Findings

Fuzzy-BML effectively models fuzzy reasoning in CBR.

The approach handles vague and uncertain information naturally.

It demonstrates improved retrieval accuracy in complex systems.

Abstract

The treatment of complex systems often requires the manipulation of vague, imprecise and uncertain information. Indeed, the human being is competent in handling of such systems in a natural way. Instead of thinking in mathematical terms, humans describes the behavior of the system by language proposals. In order to represent this type of information, Zadeh proposed to model the mechanism of human thought by approximate reasoning based on linguistic variables. He introduced the theory of fuzzy sets in 1965, which provides an interface between language and digital worlds. In this paper, we propose a Boolean modeling of the fuzzy reasoning that we baptized Fuzzy-BML and uses the characteristics of induction graph classification. Fuzzy-BML is the process by which the retrieval phase of a CBR is modelled not in the conventional form of mathematical equations, but in the form of a database with membership functions of fuzzy rules.