Using Concept Learning for Knowledge Acquisition

Although experts have difficulty formulating their knowledge explicitly as rules, they find it easy to demonstrate their expertise in specific situations. Schemes for learning concepts from examples offer the potential for domain experts to interact directly with machines to transfer knowledge. Concept learning methods divide into similarity-based, hierarchical, function induction, and explanation-based knowledge-intensive techniques. These are described, classified according to input and output representations, and related to knowledge acquisition for expert systems. Systems discussed include candidate elimination, version space, ID3, PRISM, MARVIN, NODDY, BACON, COPER, and LEX-II. Teaching requirements are also analysed.

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