Introduction To Knowledge Systems

Focusing on fundamental scientific and engineering issues, this book communicates the principles of building and using knowledge systems from the conceptual standpoint as well as the practical. Previous treatments of knowledge systems have focused on applications within a particular field, or on symbol-level representations, such as the use of frame and rule representations. Introduction to Knowledge Systems presents fundamentals of symbol-level representations including representations for time, space, uncertainty, and vagueness. It also compares the knowledge-level organizations for three common knowledge-intensive tasks: classification, configuration, and diagnosis. The art of building knowledge systems incorporates computer science theory, programming practice, and psychology. The scope of this book is appropriately broad, ranging from the design of hierarchical search algorithms to techniques for acquiring the task-specific knowledge needed for successful applications. Each chapter proceeds from concepts to applications, and closes with a brief tour of current research topics and open issues. Readers will come away with a solid foundation that will enable them to create real-world knowledge systems using whatever tools and programming languages are most current and appropriate.

An Introduction to Knowledge Engineering presents a simple but detailed exp- ration of current and established work in the ?eld of knowledge-based systems and related technologies. Its treatment of the increasing variety of such systems is designed to provide the reader with a substantial grounding in such techno- gies as expert systems, neural networks, genetic algorithms, case-based reasoning systems, data mining, intelligent agents and the associated techniques and meth- ologies. The material is reinforced by the inclusion of numerous activities that provide opportunities for the reader to engage in their own research and re?ection as they progress through the book. In addition, self-assessment questions allow the student to check their own understanding of the concepts covered. The book will be suitable for both undergraduate and postgraduate students in computing science and related disciplines such as knowledge engineering, arti?cial intelligence, intelligent systems, cognitive neuroscience, robotics and cybernetics. vii Contents Foreword vii 1 An Introduction to Knowledge Engineering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Section 1: Data, Information and Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Section 2: Skills of a Knowledge Engineer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Section 3: An Introduction to Knowledge-Based Systems. . . . . . . . . . . . . . . . . 18 2 Types of Knowledge-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Section 1: Expert Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Section 2: Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Section 3: Case-Based Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Section 4: Genetic Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Section 5: Intelligent Agents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Section 6: Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3 Knowledge Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4 Knowledge Representation and Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Section 1: Using Knowledge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Section 2: Logic, Rules and Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Section 3: Developing Rule-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Section 4: Semantic Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Offering an introduction to the field of expert/knowledge based systems, this text covers current and emerging trends as well as future research areas. It considers both the system shell and programming environment approaches to expert system development.;College or university bookshops may order five or more copies at a special student price. Price is available on request.

At present one of the main obstacles to a broader application of expert systems is the lack of a theory to tell us which problem-solving methods areavailable for a given problem class. Such a theory could lead to significant progress in the following central aims of the expert system technique: - Evaluating the technical feasibility of expert system projects: This depends on whether there is a suitable problem-solving method, and if possible a corresponding tool, for the given problem class. - Simplifying knowledge acquisition and maintenance: The problem-solving methods provide direct assistance as interpretation models in knowledge acquisition. Also, they make possible the development of problem-specific expert system tools with graphical knowledge acquisition components, which can be used even by experts without programming experience. - Making use of expert systems as a knowledge medium: The structured knowledge in expert systems can be used not only for problem solving but also for knowledge communication and tutorial purposes. With such a theory in mind, this book provides a systematic introduction to expert systems. It describes the basic knowledge representations and the present situation with regard tothe identification, realization, and integration of problem-solving methods for the main problem classes of expert systems: classification (diagnostics), construction, and simulation.

Knowledge has only recently been widely recognized as an organizational asset, the effective management of which can afford a firm competitive advantage. This book takes an interdisciplinary approach to knowledge management relating it to business strategy, dynamic capabilities and firm performance. Some of the most eminent scholars in management have contributed to this timely book, including John Seely Brown, Chris Argyris, Georg von Krogh, Soumitra Dutta, Howard Thomas and John McGee, Arie Lewin and Silvia Massini. The book offers practitioners and students alike state of the art research in the field of organizational knowledge and management

Health care systems worldwide are faced with the challenge of improving the quality of care. Providing evidence from health research is necessary but not sufficient for the provision of optimal care and so knowledge translation (KT), the scientific study of methods for closing the knowledge-to-action gap and of the barriers and facilitators inherent in the process, is gaining significance. Knowledge Translation in Health Care explains how to use research findings to improve health care in real life, everyday situations. The authors define and describe knowledge translation, and outline strategies for successful knowledge translation in practice and policy making. The book is full of examples of how knowledge translation models work in closing the gap between evidence and action. Written by a team of authors closely involved in the development of knowledge translation this unique book aims to extend understanding and implementation worldwide. It is an introductory guide to an emerging hot topic in evidence-based care and essential for health policy makers, researchers, managers, clinicians and trainees.