Heterogeneous knowledge representation using a finite automaton and first order logic: a case study in electromyography

TL;DR

This paper presents a novel heterogeneous knowledge representation combining finite automata and first order logic to improve expert systems for electromyography diagnosis, addressing challenges in modeling complex human cognitive functions.

Contribution

It introduces a new hybrid knowledge representation method integrating automata and logic, tailored for electromyography analysis in medical expert systems.

Findings

Developed a knowledge representation framework for electromyography

Designed an expert system architecture using the hybrid model

Addressed knowledge modeling challenges in medical diagnosis

Abstract

In a certain number of situations, human cognitive functioning is difficult to represent with classical artificial intelligence structures. Such a difficulty arises in the polyneuropathy diagnosis which is based on the spatial distribution, along the nerve fibres, of lesions, together with the synthesis of several partial diagnoses. Faced with this problem while building up an expert system (NEUROP), we developed a heterogeneous knowledge representation associating a finite automaton with first order logic. A number of knowledge representation problems raised by the electromyography test features are examined in this study and the expert system architecture allowing such a knowledge modeling are laid out.