Metacognition in computation: a selected research review
Artificial Intelligence
Field review: Metacognition in computation: A selected research review
Artificial Intelligence
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Knowledge engineers are efficient, active leamers. They systematically approach domains and acquire knowledge to solve routine, practical problems. By modeling their methods, we may develop a basis for teaching other students how to direct their own learning. In particular, a knowledge engineer is good at detecting gaps in a knowledge base and asking focused questions to improve an expert system''s performance. This ability stems from domain-general knowledge about: problem-solving procedures, the categorization of routine problem-solving knowledge, and domain and task differences. this paper studies these different forms of metaknowledge, and illustrates its incorporation in an intelligent tutoring system. A model of learning is presented that describes how the knowledge engineer detects problem-solving failures and tracks them back to gaps in domain knowledge, which are then reformulated as questions to ask a teacher. We describe how this model of active learning is being developed and tested in a knowledge acquisition program for an expert system.