This volume is an outgrowth of a NATO Advanced Research Workshop on "Expert Judgment and Expert Systems," held in Porto, Portugal, August 1986. Support for the Workshop was provided by the NATO Division of Scientific Affairs, the U.S. Army Research Institute, and the U.S. National Science Foundation. The Workshop brought together researchers from the fields of psychology, decision analysis, and artificial intelligence. The purposes were to assess similarities, differences, and complementarities among the three approaches to the study of expert judgment; to evaluate their relative strengths and weaknesses; and to propose profitable linkages between them. Each of the papers in the present volume is directed toward one or more of those goals. We wish to express our appreciation and thanks to the following persons for their support and assistance: John Adams, Vincent T. Covello, Luis da Cunha, Claire Jeseo, B. Michael Kantrowitz, Margaret Lally, Judith Orasanu, R. M. Rodrigues, and Sandor P. Schuman.

This book presents a framework for building intelligent systems based on the mathematical decision models of Decision Analysis. The author provides new techniques for automated explanation and knowledge acquisition in formally sound systems that reason about complex tradeoffs in decisions. Also included are specifications for implementing these techniques in computer programs, along with demonstration applications in marketing, process control, and medicine. Readers with an interest in artificial intelligence will gain a foundation for building formally justifiable, intelligible, modifiable systems for computing decisions involving multiple considerations, with applications across a variety of domains. Beyond decision models, the methodology of the work reported suggests a more general approach to employing formal mathematical models in transparent intelligent systems. Decision-analysis experts will find a collection of methods for explaining decision-analytic advice to clients in intuitive terms, for simplifying parameter assessment, and for managing changing preferences over time. The book provides sufficient background material to promote understanding by readers who may be unfamiliar with artificial intelligence, with decision analysis, or with both fields, and such material is labeled to increase the well-versed reader's efficiency in skipping particular sections.

This book introduces methods for uncertain multi-attribute decision making including uncertain multi-attribute group decision making and their applications to supply chain management, investment decision making, personnel assessment, redesigning products, maintenance services, military system efficiency evaluation. Multi-attribute decision making, also known as multi-objective decision making with finite alternatives, is an important component of modern decision science. The theory and methods of multi-attribute decision making have been extensively applied in engineering, economics, management and military contexts, such as venture capital project evaluation, facility location, bidding, development ranking of industrial sectors and so on. Over the last few decades, great attention has been paid to research on multi-attribute decision making in uncertain settings, due to the increasing complexity and uncertainty of supposedly objective aspects and the fuzziness of human thought. This book can be used as a reference guide for researchers and practitioners working in e.g. the fields of operations research, information science, management science and engineering. It can also be used as a textbook for postgraduate and senior undergraduate students.

In the two decades since its inception by L. Zadeh, the theory of fuzzy sets has matured into a wide-ranging collection of concepts, models, and tech niques for dealing with complex phenomena which do not lend themselves to analysis by classical methods based on probability theory and bivalent logic. Nevertheless, a question which is frequently raised by the skeptics is: Are there, in fact, any significant problem areas in which the use of the theory of fuzzy sets leads to results which could not be obtained by classical methods? The approximately 5000 publications in this area, which are scattered over many areas such as artificial intelligence, computer science, control engineering, decision making, logic, operations research, pattern recognition, robotics and others, provide an affirmative answer to this question. In spite of the large number of publications, good and comprehensive textbooks which could facilitate the access of newcomers to this area and support teaching were missing until recently. To help to close this gap and to provide a textbook for courses in fuzzy set theory which can also be used as an introduction to this field, the first volume ofthis book was published in 1985 [Zimmermann 1985 b]. This volume tried to cover fuzzy set theory and its applications as extensively as possible. Applications could, therefore, only be described to a limited extent and not very detailed.

The volume contains revised versions of papers presented at the 15th Annual Meeting of the "Gesellschaft f}r Klassifika- tion". Papers were arranged in the following three parts which were the main streams of discussion during the confe- rence: 1. Data Analysis, Classification 2. Data Modeling, Knowledge Processing, 3. Applications, Special Subjects. New results on developing mathematical and statistical methods allowing quantitative analysis of data are reported on. Tools for representing, modeling, storing and processing da- ta and knowledge are discussed. Applications in astro-phycics, archaelogy, biology, linguistics, and medicine are presented.

Over the past two decades, many advances have been made in the decision support system (DSS) field. They range from progress in fundamental concepts, to improved techniques and methods, to widespread use of commercial software for DSS development. Still, the depth and breadth of the DSS field continues to grow, fueled by the need to better support decision making in a world that is increasingly complex in terms of volume, diversity, and interconnectedness of the knowledge on which decisions can be based. This continuing growth is facilitated by increasing computer power and decreasing per-unit computing costs. But, it is spearheaded by the multifaceted efforts of DSS researchers. The collective work of these researchers runs from the speculative to the normative to the descriptive. It includes analysis of what the field needs, designs of means for meeting recognized needs, and implementations for study. It encompasses theoretical, empirical, and applied orientations. It is concerned with the invention of concepts, frameworks, models, and languages for giving varied, helpful perspectives. It involves the discovery of principles, methods, and techniques for expeditious construction of successful DSSs. It aims to create computer-based tools that facilitate DSS development. It assesses DSS efficacy by observing systems, their developers, and their users. This growing body of research continues to be fleshed out and take shape on a strong, but still-developing, skeletal foundation.