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

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. Keywords: Medical expert systems, Heterogeneous knowledge representation, Finite automata, Electromyography. 1. Introduction The various kinds of knowledge and reasoning used in expert systems (ES) have been carefully analyzed and classified over several years [6,11,17]. Nevertheless some types of knowledge remain difficult to represent by means of classical structures (production rules, frames, semantic nets, etc.) commonly used in expert systems.