Learning without case records: a mapping of the repertory grid technique onto knowledge acquisition from examples

Abstract

Abstract: In building a knowledge‐based system, it is sometimes possible to save time by applying some machine learning process to a set of historical cases. In some problem domains, however, such cases may not be available. In addition, the classes, attributes and attribute values that comprise the partial domain model in terms of which cases are expressed may also not be available explicitly. In these circumstances, the repertory grid technique offers a single process for both building a partial domain model and generating a training set of examples. Alternatively, examples can be elicited directly. This paper explores the relationship between knowledge acquisition from examples and the repertory grid technique, and discusses the shared need for machine learning. Fragments of business‐strategy knowledge are used to illustrate the discussion. Copyright © 1992, Wiley Blackwell. All rights reserved