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This book presents a significant advancement in the theory and practice of knowledge engineering, the discipline concerned with the development of systems that use expert knowledge and reasoning to solve complex problems. It covers the main stages in the development of a knowledge-based system: understanding the application domain, modeling problem solving in that domain, developing the ontology and the reasoning rules, and testing the system. The book focuses on a special class of systems - learning assistants for evidence-based reasoning that learn complex problem solving expertise directly from human experts, support experts and non-experts in problem solving and decision making, and teach their problem solving expertise to students. A powerful learning agent shell, Disciple-EBR, is included with the book, enabling students, practitioners, and researchers to rapidly develop learning assistants in a wide variety of domains that require evidence-based reasoning, including intelligence analysis, cyber security, law, forensics, medicine, and education.
