APPRENTICESHIP LEARNING IN IMPERFECT DOMAIN THEORIES [chapter]

Gheorghe Tecuci, Yves Kodratoff
1990 Machine Learning  
This chapter presents DISCIPLE, a multi-strategy integrated learning system illustrating a theory and a methodology for learning expert knowledge in the context of an imperfect domain theory. DISCIPLE integrates a learning system and an empty expert system, both using the same knowledge base. It is initially provided with an imperfect (nonhomogeneous) domain theory and learns problem solving rules from the problem solving steps received from its expert user, during interactive problem solving
more » ... ssions. In this way, DISCIPLE evolves from a helpful assistant in problem solving to a genuine expert. The problem solving method of DISCIPLE combines problem reduction, problem solving by constraints, and problem solving by analogy. The learning method of DISCIPLE depends of its knowledge about the problem solving step (the example) from which it learns. In the context of a complete theory about the example, DISCIPLE uses explanation-based learning to improve its performance. In the context of a weak theory about the example, it synergistically combines explanation-based learning, learning by analogy, empirical learning, and learning by questioning the user, developing its competence. In the context of an incomplete theory about the example, DISCIPLE learns by combining the above mentioned methods, improving both its competence and performance.
doi:10.1016/b978-0-08-051055-2.50028-6 fatcat:f25bne7gkfaplifraikje4mwae