A FRAMEWORK FOR KNOWLEDGE ACQUISITION THROUGH TECHNIQUES OF CONCEPT LEARNING

Abstract

Knowledge-based systems must represent information abstractly so that it can be stored and manipulated effectively. Schemes for learning suitable representations--or concepts--from examples promise domain experts direct interaction with machines to transfer their knowledge. This paper develops an integrative framework for describing concept learning techniques which enables their relevance to knowledge engineering to be evaluated. The framework provides a general basis for relating concept learning to knowledge acquisition, and is a starting point for the development of formal design rules. The paper first frames concept learning in the context of knowledge acquisition. It then discusses the general forms of input and concept representation: as logic, functions and procedures. Next, methods of biasing the search for a suitable concept are described and illustrated: background knowledge, conceptual bias, composition bias, and preference orderings. Then modes of teacher interaction are reviewed: the nature of examples given, and the method of presenting them. Finally the framework is illustrated by applying it to the better-documented concept learning systems.