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.

Researchers have revealed that real expertise, while applied to well-defined tasks with highly circumscribed contexts, often stretches beyond its routine boundaries. For example, a medical doctor may be called upon to diagnose a rare disease or perform emergency surgery outside his or her area of specialization because other experts are not availab

This volume investigates our ability to capture, and then apply, expertise. In recent years, expertise has come to be regarded as an increasingly valuable and surprisingly elusive resource. Experts, who were the sole active dispensers of certain kinds of knowledge in the days before AI, have themselves become the objects of empirical inquiry, in which their knowledge is elicited and studied -- by knowledge engineers, experimental psychologists, applied psychologists, or other experts -- involved in the development of expert systems. This book achieves a marriage between experimentalists, applied scientists, and theoreticians who deal with expertise. It envisions the benefits to society of an advanced technology for capturing and disseminating the knowledge and skills of the best corporate managers, the most seasoned pilots, and the most renowned medical diagnosticians. This book should be of interest to psychologists as well as to knowledge engineers who are "out in the trenches" developing expert systems, and anyone pondering the nature of expertise and the question of how it can be elicited and studied scientifically. The book's scope and the pivotal concepts that it elucidates and appraises, as well as the extensive categorized bibliographies it includes, make this volume a landmark in the field of expert systems and AI as well as the field of applied experimental psychology.

This volume brings together a range of contributors from Europe and North America. All contributions were especially commissioned with a view to e- cidating a major multidisciplinary topic that is of concern to both academics and practitioners. The focus of the book is on expert judgment and its interaction with decision support systems. In the first part, the nature of expertise is discussed and characteristics of expert judges are described. Issues concemed with the eval- tion of judgment in the psychological laboratory are assessed and contrasted with studies of expert judgment in ecologically valid contexts. In addition, issues concerned with eliciting and validating expert knowledge are discussed. Dem- strations of good judgmental performance are linked to situational factors such as feedback cycles, and measurement of coherence and reliability in expert ju- ment is introduced as a baseline determinant of good judgmental performance. Issues concerned with the representation of elicited expert knowledge in kno- edge-based systems are evaluated and methods are described that have been shown to produce improvements in judgmental performance. Behavioral and mathematical ways of combining judgments from multiple experts are compared and contrasted. Finally, the issues developed in the preceding contributions are focused on current controversies in decision support. Expert judgment is utilized as a major input into decision analysis, forecasting with statistical models, and expert s- tems.

The twenty-seven papers cover recent advances in both empirical and theoretical aspects of man-machine interaction with special emphasis on the subjects of man-automation and man-computer interaction. They provide information on a subject which has grown rapidly in importance during recent years.

Like its companion, this volume contains a wide range of material representing foundational work in knowledge acquisition problems, techniques and tools from the major research groups worldwide. Volume two includes tool-oriented material.

As the ergonomic aspect of many problems facing the industry today attracts more attention from the management, providing scientific knowledge and the know-how to solve such problems is becoming increasingly more important. The impetus for this book originated from the pressing need to make the state-of-the-art ergonomic information on workspace, equipment and tool design available to practising ergonomists, safety specialists, engineering designers, and business and technical managers.The book reinforces the notion that ergonomic data should be explicitly integrated in the design of a system, and should become an indispensable part of the overall design process in production engineering, on an equal basis with such activities as mechanical component design, quality assurance, maintenance, inspection, etc. The focus is on selected ergonomic data for workspace, equipment and tool design, with special emphasis on the practical aspects of applying the available information to specific problem areas.

The fourth in the Inside series, this volume includes four theses completed under the editor's direction at the Institute for the Learning Sciences at Northwestern University. This series bridges the gap between Schank's books introducing (for a popular audience) the theories behind his work in artificial intelligence (AI) and the many articles and books written by Schank and other AI researchers for their colleagues and students. The series will be of interest to graduate students in AI and professionals in other academic fields who seek the retraining necessary to join the AI effort or to understand it at the professional level. This volume elaborates the Case-Based Teaching Architecture. A central tenet of this architecture is the importance of acquiring cases, and being able to retrieve and use those cases to solve new problems. The theses address the problems of building case bases, indexing large amounts of data contained within those case bases, and retrieving information on a need-to-know basis. They also reflect the work of researchers at the Institute to design tools that enable software programs to be built more effectively and efficiently.

"This book presents a modern view of intelligent tutoring, focusing mainly on the conception of these systems according to a multi-agent approach and on the affective and cognitive modeling of the student in this kind of educational environment"--Provided by publisher.