The first book to discuss efficient ways to implement the systems currently being developed--written by the co-author of Expert Systems: Artificial Intelligence in Business, generally regarded as the best non-technical guide to expert systems for business people. Gives innovative ideas for using expert systems to facilitate business operations. Appropriate as a text or supplement for data base, decision support, or special-topic courses that cover expert systems. Clearly explains new applications of automatic decision-making in management, sales, operations, programming, research, and service industries. Text supported by extensive examples and graphs.
Presents a step-by-step methodology for designing expert systems. Each chapter on design methodology starts with a problem and leads the reader through the design of a system which solves that problem.
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 represent intelligent systems based on knowledge modeling, symbolic representation and its storage which allows for an unconventional method approach of complex applied analysis, sorting planning, design and diagnosis of problems in different areas of activity where an algorithmic description cannot be fully accomplished. Presently, the most intelligent systems from organizations are expert systems developed in accordance with fast prototyping methodology. Expert systems are used in a variety of areas, including, technical, banking, industrial and other professional areas. This book includes recent advances in expert systems research.
Knowledge Processing and Applied Artificial Intelligence discusses the business potential of knowledge processing and examines the aspects of applied artificial intelligence technology. The book is comprised of nine chapters that are organized into five parts. The text first covers knowledge processing and applied artificial intelligence, and then proceeds to tackling the techniques for acquiring, representing, and reasoning with knowledge. The next part deals with the process of creating and implementing strategically advantageous knowledge-based system applications. The fourth part covers intelligent interfaces, while the last part details alternative approaches to knowledge processing. The book will be of great use to students and professionals of computer or business related disciplines.
Expert Systems are so far the most promising achievement of artificial intelligence research. Decision making, planning, design, control, supervision and diagnosis are areas where they are showing great potential. However, the establishment of expert system technology and its actual industrial impact are still limited by the lack of a sound, general and reliable design and construction methodology.This book has a dual purpose: to offer concrete guidelines and tools to the designers of expert systems, and to promote basic and applied research on methodologies and tools. It is a coordinated collection of papers from researchers in the USA and Europe, examining important and emerging topics, methodological advances and practical experience obtained in specific applications. Each paper includes a survey introduction, and a comprehensive bibliography is provided.
This book is written for software engineers, software project leaders, and software managers who would like to introduce a new advanced software technology, expert systems, into their product. Expert system technology brings into programming a new dimension in which "rule of thumb" or heuristic expert knowledge is encoded in the program. In contrast to conventional procedural languages {e. g. , Fortran or C}, expert systems employ high-level programming languages {Le. , expert system shells} that enable us to capture the judgmental knowledge of experts such as geologists, doctors, lawyers, bankers, or insurance underwriters. Past expert systems have been more successfully applied in the problem areas of analysis and synthesis where the boundary of lo;nowledge is well defined and where experts are available and can be identified. Early successful applications include diagnosis systems such as MYCIN, geological systems such as PROSPECTOR, or design/configu ration systems such as XC ON. These early expert systems were mainly applicable to scientific and engineering problems, which are not theoreti cally well understood in terms of decisionmaking processes by their experts and which therefore require judgmental assessment. The more recent expert systems are being applied to sophisticated synthesis problems that involve a large number of choices, such as how the elements are to be compared. These problems normally entailed a large search space and slower speed for the expert systems designed. Examples of these systems include factory scheduling applications such as ISIS, or legal reasoning applications such as TAXMAN.
UML for Developing Knowledge Management Systems provides knowledge engineers the framework in which to identify types of knowledge and where this knowledge exists in an organization. It also shows ways in which to use a standard recognized notation to capture, or model, knowledge to be used in a knowledge management system (KMS). This volume
