Learning in the large: case-based software systems design
Stuart H. Rubin · 2002
The author describes a novel approach to the development of a knowledge-based software assistant (KBSA) by applying artificial intelligence in an integrated rather than a supportive role; that is, an attempt is made to offer a framework for unifying case-based reasoning (CBR) with object-oriented rule-based systems, for unifying man-machine systems with learning, and for unifying object-oriented analogical reasoning with constrained search. The technique is called constrained-set generalization (CSG). The CSG technique emphasizes the importance of the man-machine interface in learning heuristics. It also purports a computational theory of creativity, which is based upon the object-oriented concept of set analogs. CSG has been applied to the problem of generalizing and reusing software fragments.>