Inhaltsangabe
Knowledge acquisition (KA) and machine learning (ML) share a common goal of acquiring and organizing the knowledge of knowledge-based system, yet until recently, the research on each has been done in isolation. In particular, practitioners use techniques from both fields in building knowledge-based systems, and are often frustrated by the lack of integration between the two. 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 toward an integrated approach using machine learning techniques to automate knowledge acquisition from experts, and using 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 realworld applications will make it essential reading for students, researchers, and practitioners working in machine learning and knowledge acquisition. Includes an introduction to knowledge acquisition and machine learning. Presents methods for automating the knowledge acquisition process through the use of machine learning techniques. Outlines ways to enhance the power of learning methods through the employment of knowledge acquisition techniques. Describes successful practical applications of integrated knowledge acquisition and machine learning approaches.
Reseña del editor
Knowledge acquisition (KA) and machine learning (ML) share a common goal of acquiring and organizing the knowledge of knowledge-based system, yet until recently, the research on each has been done in isolation. In particular, practitioners use techniques from both fields in building knowledge-based systems, and are often frustrated by the lack of integration between the two. 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 toward an integrated approach using machine learning techniques to automate knowledge acquisition from experts, and using 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 realworld applications will make it essential reading for students, researchers, and practitioners working in machine learning and knowledge acquisition. Includes an introduction to knowledge acquisition and machine learning. Presents methods for automating the knowledge acquisition process through the use of machine learning techniques. Outlines ways to enhance the power of learning methods through the employment of knowledge acquisition techniques. Describes successful practical applications of integrated knowledge acquisition and machine learning approaches.
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