Readings in Knowledge Acquisition and Learning collects the best of the artificial intelligence literature from the fields of machine learning and knowledge acquisition. This book brings together for the first time the perspectives on constructing knowledge-based systems from these two historically separate subfields of artificial intelligence. A key criterion for article inclusion is an empirical demonstration that the method described in the paper successfully automates some important aspect of creating and maintaining a knowledge-based system. In addition to the papers, the editors provide an introduction to the field and to each group of papers, discussing their significance and pointing to related work. This book can serve as a text for courses and seminars in artificial intelligence, expert systems, knowledge acquisition, knowledge engineering, and machine learning. It will also provide practical ideas for professionals engaged in the building and maintenance of knowledge-based systems.
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