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.
