Machine learning and knowledge acquisition represent two complementary approaches to the acquisition and organization of knowledge for knowledge-based systems. Machine learning has focused on developing autonomous algorithms for acquiring knowledge as data and for knowledge compilation and organization. In contrast, knowledge acquisition has focused on improving and partially automating the acquisition of knowledge from human experts by knowledge engineers. Currently, both fields are moving towards an integrated approach using machine learning techniques to automate knowledge acquisition from experts, and knowledge acquisition techniques to guide and assist the learning process. This is the first book to present some of the most representative approaches to the integration of machine learning and knowledge acquisition such as case-based reasoning, apprenticeship learning, knowledge base refinement through multistrategy learning, example-guided knowledge based revision, and interactive inductive logic programming. It also presents their application to such areas as planning, scheduling, diagnosis, control, information retrieval and robotics. The book's tutorial style and description of real-world applications will make it essential reading for students, researchers and practitioners working in machine learning and knowledge acquisition.