Managing Expert Systems

3. Textbook for a course in expert systems,if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe "weekend mechanic" books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal ing with the phenomenon are then presented. The chapter ends with the descrip tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre sent knowledge in expert systems are presented: first-order logic, production sys tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented.

This six-volume set presents cutting-edge advances and applications of expert systems. Because expert systems combine the expertise of engineers, computer scientists, and computer programmers, each group will benefit from buying this important reference work. An "expert system" is a knowledge-based computer system that emulates the decision-making ability of a human expert. The primary role of the expert system is to perform appropriate functions under the close supervision of the human, whose work is supported by that expert system. In the reverse, this same expert system can monitor and double check the human in the performance of a task. Human-computer interaction in our highly complex world requires the development of a wide array of expert systems. Expert systems techniques and applications are presented for a diverse array of topics including Experimental design and decision support The integration of machine learning with knowledge acquisition for the design of expert systems Process planning in design and manufacturing systems and process control applications Knowledge discovery in large-scale knowledge bases Robotic systems Geograhphic information systems Image analysis, recognition and interpretation Cellular automata methods for pattern recognition Real-time fault tolerant control systems CAD-based vision systems in pattern matching processes Financial systems Agricultural applications Medical diagnosis

Managing Expert Systems explores the trends in expert systems development and implementation. As top authorities in the field of ES, Turban and Liebowitz examine the factors that contribute to the development of a successful expert system.

Expert systems can be used on PCs. This book provides managers with a readable and realistic view of what expert systems are and how they can be used profitably on microcomputers. Covers everything they need to know about purchasing, developing, implementing, and using an expert system. The book's format makes for easy reference--each chapter can be read independently. Provides descriptions of the commercially available software--and all systems described in this book have been run by the author. Whether you're just choosing a shell for purchase, or want to set-up a complete system from scratch, the real-world examples and practical advice will make the job easier.

Presents a practical, step-by-step approach to developing and managing expert systems in business and industry.

Decision support systems; management support systems: an overview; decision making, systems, modeling, and support; data management; modeling and model management; user interface; constructing a decision support system; organizational DSS and advanced topics; enterprise support systems; Group decision support systems; executive information and support systems; fundamentals of artificial intelligence and expert systems; applied artificial intelligence: an overview; fundamentals of expert systems; knowledge acquisition and validation; knowledge representation; inferences, explanations, and uncertainty; building expert systems: process and tools; cutting edge decision support technologies; neural computing: the basics; neural computing applications, genetic algorithms, and fuzzy logic; integrating and implementation; integrating decision support technologies; Implementing management support systems; organizational and societal impacts of management support systems; appendix and student project.

Knowledge: In the realm of knowledge management, information plus wisdom equals knowledge. Organizations have found that the knowledge they contain can be one of their most important competitive weapons Definition: Knowledge management: The ability of an organization to manage, store, value, and distribute knowledge. Some organizations have created the position of Chief Knowledge Manager (CKM) to handle knowledge management responsibilities Many organizations fail to effectively manage and use the most important competitive edge they possess - their knowledge and "intellectual capital." This book covers the entire growing field of knowledge management, with particular emphasis on knowledge-based systems and their use in preserving knowledge in an organization, and integrating it across departments and disciplines. This hands-on guide shows how businesses and other organizations can re-engineer their processes using an applied knowledge-based approach. Each chapter introduces a different aspect of the field and demonstrates its application in actual case studies. Examples from industry, education, and government show the wide application of this exciting new field of study. The book also covers promising trends such as learning organizations, intelligent organizations, and enterprise management.

Three major areas are examined: teamwork required to implement AI technologies, strategies for developing computer resources using AI, and tactics for overseeing implementation and day-to-day operation. Annotation copyrighted by Book News, Inc., Portland, OR