Knowledge Skill And Artificial Intelligence

Knowledge, Skill and Artificial Intelligence

Issues raised by the Theory of Knowledge, a central theme in the development of Artificial Intelligence, are the main topic of this book. The major questions are: How is the expert's knowledge to be elicited, what are the limits and possibilities? How can skill be developed and maintained in a more and more computerized and abstract working life? This last question is also closely related to the discussion on programs for education and training in society and working life. Long term effects on skill formation in working life in relation to new technology are a very important area of research. Case studies form the basis for philosophical reflections with the main concept of tacit knowledge as the central issue of skill and new technology. To a great extent the discussion is based on current case studies of professional groups with experience in advanced computer technology. The contributions of this book demonstrate the complicated nature of human knowledge. They introduce different theoretical perspectives on the issue of knowledge acquisition and elicitation.

Understanding the impact of artificial intelligence on skills development

Knowledge Graphs for Explainable Artificial Intelligence: Foundations, Applications and Challenges

The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.