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2.2. FOUNDATION OF OBJECT-ORIENTED EXPERT SYSTEMSIn recent years, the AI community has shown increasing interest in object-oriented programming (OOP). For instance, a number of extensions have been made to conventional AI languages (PROLOG, LISP) to integrate object-oriented concepts. Such interest reflects a belief that the OOP can play an important role in the task of designing large expert systems. Some expert system applications have dealt with hybrid representations, focusing on synergies and possible conflicts between OOP and traditional rule-based representation. Such hybrid representation systems can supplement the limited power of production rules with objects, which capture declarative knowledge in a hierarchy. Consequently, the V&V techniques that have been developed for rule bases have been adapted to deal with the kind of anomalies inherent in such hybrid systems. In this section we provide a formulation of anomalies in hybrid systems that combine objects and rules. We define an object-oriented expert system (OOES) as a non-flat expert system that performs its reasoning (rules, pattern matching, making inferences) in a framework of structured classes of objects (the pattern matching is performed on objects). Such a formulation -- not an easy task as the traditional formulation of the anomalies described in the previous section -- specific to rule-based knowledge representation, which greatly restricts applicability. A better approach is to perform anomaly detection at the level of a conceptual model of the KB, rather than at the implementation level. The conceptual model underlying our approach is derived from the KADS model of expertise (Wielinga et al., 1992) and has been described previously (Vermesan, 1996). Inferences are the principal building blocks of the model, as they are in general for KADS expertise models. The inference layer of the model describes the possible inferences that can be drawn on the basis of the domain knowledge. An inference is a declarative definition of the directional relationship between input knowledge roles and output knowledge roles. Knowledge roles are the knowledge elements on which inferences operate. In an OOES, knowledge roles are in fact objects, and in our graphical notation they are represented three-dimensionally. We have deliberately changed the graphical notation of the KADS knowledge roles to highlight the fact that knowledge roles are complex objects, embedding procedural knowledge, and among which there exist subtyping relationships. Some definitions are needed before proceeding further.
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