A Hybrid Knowledge-Based System for
Technical Diagnosis Learning and Assistance

David J. Macchion and Dinh P. Vo

Laboratoire ARAMIIHS
Matra Marconi Space
ZI du Palays
31, Rue des Cosmonautes
31077 Toulouse Cedex, France
macchion@music.matra-espace.fr


Abstract. This paper sets out the design of a fault diagnosis system combining Model-Based, 
Case-Based and Rule-Based Reasoning techniques. Within the Model-Based
layer, domain concepts are organized in hierarchies; different aspects of the system to be
diagnosed are presented in a technical model; the Model-Based inference engine consists
of basic principles operating on the technical model. Within the Case-Based layer,
Model-Based or instructor processed resolutions are stored in a memory of past incident
cases; indexes of various influences and more or less constraining viewpoints are invoked
by the Case-Based inference engine in order to retrieve relevant cases quickly;
explanations and adaptation mles are then used to make case description match and adapt
case resolution. Within the Rule-Based layer, situation rules synthesizing incident
description and validation rules supporting diagnosis assessment are triggered by the
Rule-Based inference engine to solve well-tried, frequent or trivial problems. Integrating
these knowledge layers into a unified model enhances the scope of the resultant
knowledge base. Combining these reasoning modes into a coherent control strategy
improves the efficiency of the target Knowledge-Based System.
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