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

Authors:
Vincent Rialle;Annick Vila;Yves Besnard
Affiliations:
Faculté de Médecine de Grenoble, Département de Mathématiques, Statistiques et Informatique Médicale, Lab. TIM-B, F-38706 La Tronche Cedex, France;Centre Hospitalier et Universitaire de Grenoble, Laboratoire d'Electromyographie, Service EFSN, B.P. 217 X, F-38043 Grenoble Cedex, France;Centre Hospitalier et Universitaire de Grenoble, Laboratoire d'Electromyographie, Service EFSN, B.P. 217 X, F-38043 Grenoble Cedex, France
Venue:
Artificial Intelligence in Medicine
Year:
1991

Citing 3
Cited 1

Conceptual structures: information processing in mind and machine

Conceptual structures: information processing in mind and machine
The "Software Engineering" of Expert Systems: Is Prolog Appropriate?

IEEE Transactions on Software Engineering - Special issue on artificial intelligence and software engineering
Heuristic classification

Artificial Intelligence

Paper: Multiply sectioned Bayesian networks for neuromuscular diagnosis

Artificial Intelligence in Medicine

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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 electrophysiological test features are examined in this study and the expert system architecture allowing such a knowledge modeling is laid out.