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In a certain number of situations, human cognitive functioning is difficultto represent with classical artificial intelligence structures. Such adifficulty arises in the polyneuropathy diagnosis which is based on the spatialdistribution, along the nerve fibres, of lesions, together with the synthesisof several partial diagnoses. Faced with this problem while building up anexpert system (NEUROP), we developed a heterogeneous knowledge representationassociating a finite automaton with first order logic. A number of knowledgerepresentation problems raised by the electromyography test features areexamined in this study and the expert system architecture allowing such aknowledge modeling are laid out.

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