Learning Control Knowledge in Models of Expertise ECML'95 Workshop on Knowledge-Level Modelling and Machine Learning
نویسنده
چکیده
During the development and the life-cycle of knowledge-based systems the requirements on the system and the knowledge in the system will change. One of the types of knowledge aaected by changing requirements is control-knowledge, which prescribes the ordering of problem-solving steps. Machine-learning can aid developers of knowledge-based systems in adapting their systems to changing requirements. A number of machine-learning techniques for learning control-knowledge have been applied to problem-solvers (Prodigy-EBL, LEX). In knowledge engineering, the focus has shifted to the construction of knowledge-level models of problem-solving instead of directly constructing a knowledge-based system in a problem-solver. In this paper we describe work in progress on how to apply machine learning techniques to the KADS model of expertise.
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