Protos: an exemplar-based learning apprentice

TLDR: Protos as discussed by the authors is a learning apprentice system for heuristic classification that relegates inductive learning and deductive problem solving to minor roles in support of retaining, indexing, and matching exemplars.

Abstract: Building Protos, a learning apprentice system for heuristic classification, has forced us to scrutinize the usefulness of inductive learning and deductive problem solving. While these inference methods have been widely studied in machine learning, their seductive elegance in artificial domains ( e.g., mathematics) does not carry over to natural domains ( e.g., medicine). This paper briefly describes our rationale in the Protos system for relegating inductive learning and deductive problem solving to minor roles in support of retaining, indexing, and matching exemplars. The problems that arise from “lazy generalization” are described along with their solutions in Protos. Finally, an example of Protos in the domain of clinical audiology is discussed.