Abstract
Exemplar-Based Knowledge Acquisition is an excellent reference document describing a promising knowledge acquisition tool, the Protos system. Since it is publicly available, Protos provides a testbed for a variety of techniques and issues in machine learning: (1) the integration of similarity-based and explanation-based approaches, (2) the transition from user guidance to autonomy, and (3) the relation between knowledge representation and efficiency. Much work remains to be done to assess Protos' scaleability and to uncover and repair any possible existing complexity problems.
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