The case-based reasoning (CBR) architecture described in this paper represents a substantive advancement in the representation of case-knowledge. It addresses three major problems found in current CBR schemes: 1) Insufficient treatment of abstract case features' context-dependent characteristics. 2) Lack of a methodical support for atomic and structured case features that contain and represent imprecisely specified quantities. 3) And little account for clustering and organising cognate cases into conceptually overlapping categories. To overcome the representational inadequacy resulting from those deficiencies, this work proposes two modelling fundamentals, namely, fuzzy primitive and fuzzy complex abstract features. These allow a flexible, polymorphic encoding of case characteristics as real numbers, linguistic terms, fuzzy numbers and fuzzy complex objects respectively. Based on this concept, it is possible to systematically organise a case base in fuzzy categories, reflecting real-world case clusters. In the presented scheme, a prototype case and its associated approximation scales form the basis to realise a versatile mechanism to represent the context-specific idiosyncrasies of fuzzy abstract case features. Case categories, fuzzy abstract features, cases, and the approximation scale concept are modelled as self-contained, operational entities. They co-operatively concert their services when they categorise an unclassified problem description (target case), and locate relevant stored cases. Applied to the Coronary Heart Disease risk assessment domain, the proposed architecture has proven to be highly adequate for capturing and efficiently processing case-knowledge. Moreover, as this scheme is designed upon well-established object-oriented principles, it has been shown that it can seamlessly integrate in a wider, more general knowledge management regime.
ASJC Scopus subject areas
- Artificial Intelligence