Using a description classifier to enhance deductive inference

Descriptions are given of some of the emerging techniques and uses of classifier-based reasoning systems, specifically as they apply to the LOOM knowledge representation system. The author asserts that current generation expert system tools fail to achieve a satisfactory integration of frame knowledge and rule knowledge. He then describes a class of languages, exemplified by LOOM, that combine descriptions and rules to form a hybrid logic that does achieve a satisfactory level of integration. The use of classifier technology enables a form of unification over descriptions that fills a gap present in the frame-plus-rule (F+R) technology. In addition, classification-based inference technology is more powerful than the inference technology found in languages such as (pure) Prolog. A classifier´s ability to automatically organize definitions and to detect many kinds of inconsistency can significantly benefit the task of knowledge acquisition. The unique capabilities of the classifier can be applied to enhance existing programming paradigms. The author highlights specific enhancements to the production rule and object-oriented programming paradigms