This paper presents the unified approach to machine learning and knowledge acquisition in the Case-Oriented Expert (COEx) system architecture. Knowledge acquisition techniques are used to delineate a problem class hierarchy for complex real-world planning tasks. This hierarchy is stepwise formalized into a KL-ONE-like representation. Explanation-based abstraction is then applied to previously solved cases, and procedure schemata are constructed for the different problem classes. COEx is a type of apprenticeship learning system where reversed engineering is applied to success cases from the real world. An evaluation of this unification reveals its conceptual soundness and several technical problems.
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