A FRAMEWORK FOR KNOWLEDGE ACQUISITION THROUGH TECHNIQUES OF CONCEPT LEARNING
Date
1988-03-01
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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.
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Computer Science