This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.
The primary aim of this monograph is to provide a formal framework for the representation and management of uncertainty and vagueness in the field of artificial intelligence. It puts particular emphasis on a thorough analysis of these phenomena and on the development of sound mathematical modeling approaches. Beyond this theoretical basis the scope of the book includes also implementational aspects and a valuation of existing models and systems. The fundamental ambition of this book is to show that vagueness and un certainty can be handled adequately by using measure-theoretic methods. The presentation of applicable knowledge representation formalisms and reasoning algorithms substantiates the claim that efficiency requirements do not necessar ily require renunciation of an uncompromising mathematical modeling. These results are used to evaluate systems based on probabilistic methods as well as on non-standard concepts such as certainty factors, fuzzy sets or belief functions. The book is intended to be self-contained and addresses researchers and practioneers in the field of knowledge based systems. It is in particular suit able as a textbook for graduate-level students in AI, operations research and applied probability. A solid mathematical background is necessary for reading this book. Essential parts of the material have been the subject of courses given by the first author for students of computer science and mathematics held since 1984 at the University in Braunschweig.
Approximate reasoning is a key motivation in fuzzy sets and possibility theory. This volume provides a coherent view of this field, and its impact on database research and information retrieval. First, the semantic foundations of approximate reasoning are presented. Special emphasis is given to the representation of fuzzy rules and specialized types of approximate reasoning. Then syntactic aspects of approximate reasoning are surveyed and the algebraic underpinnings of fuzzy consequence relations are presented and explained. The second part of the book is devoted to inductive and neuro-fuzzy methods for learning fuzzy rules. It also contains new material on the application of possibility theory to data fusion. The last part of the book surveys the growing literature on fuzzy information systems. Each chapter contains extensive bibliographical material. Fuzzy Sets in Approximate Reasoning and Information Systems is a major source of information for research scholars and graduate students in computer science and artificial intelligence, interested in human information processing.
An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development. People in computer science, especially those in artificial intelligence, knowledge-based systems, and intelligent systems will find this to be a valuable sourcebook. Engineers, particularly control engineers, will also have a strong interest in this book. Finally, the book will be of interest to researchers working in decision support systems, operations research, decision theory, management science and applied mathematics. An Introduction to Fuzzy Logic Applications in Intelligent Systems may also be used as an introductory text and, as such, it is tutorial in nature.
This book is an essay on relevant problems of epistemology (the theory of knowledge) related to computer science. It draws a continuous line between the earliest scientific approaches of epistemology, starting with the Greek Classics and the recent practical and theoretical problems of computer modelling, and by that the appropriate application of computers to our present problems. Uncertainty, logic and language are the key issues of this road leading to some new aspects of cognitive psychology and unification of the different results for a modelling procedure. The book is not a textbook but a critical survey of usual and advertised methods with an evaluation of them from the point of view of their applicability, reliability and limits. Probability, Bayesian, Dempster-Shafer, fuzzy and other approaches are treated in this way in uncertainty, different worlds' concepts, non-monotonic logic and other methods and views in logic. The emphasis in linguistics is put on the meta concept, and in cognitive applications of the pattern concept.Written mostly in an entertaining style, this book provides a more palatable reading of a profound subject.
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.
This book focuses on the role of computers in the provision of medical services. It provides both a conceptual framework and a practical approach for the implementation and management of IT used to improve the delivery of health care. Inspired by a Stanford University training program, it fills the need for a high quality text in computers and medicine. It meets the growing demand by practitioners, researchers, and students for a comprehensive introduction to key topics in the field. Completely revised and expanded, this work includes several new chapters filled with brand new material.
Cognitive science is a multidisciplinary science concerned with understanding and utilizing models of cognition. It has spawned a great dealof research on applications such as expert systems and intelligent tutoring systems, and has interacted closely with psychological research. However, it is generally accepted that it is difficult to apply cognitive-scientific models to medical training and practice. This book is based on a NATO Advanced Research Workshop held in Italy in 1991, the purpose of which was to examine the impact ofmodels of cognition on medical training and practice and to outline future research programmes relating cognition and education, and in particular to consider the potential impact of cognitive science on medical training and practice. A major discovery presented in the book is that the research areas related to artificial intelligence, cognitive psychology, and medical decision making are considerably closer, both conceptually and theoretically, than many of the workshop participants originally thought.
This major collection of short essays reviews the scope and progress of research in artificial intelligence over the past two decades. Seminal and most-cited papers from the journal Artificial Intelligence are revisited by the authors who describe how their research has been developed, both by themselves and by others, since the journals first publication.The twenty-eight papers span a wide variety of domains, including truth maintainance systems and qualitative process theory, chemical structure analysis, diagnosis of faulty circuits, and understanding visual scenes; they also span a broad range of methodologies, from AI's mathematical foundations to systems architecture.The volume is dedicated to Allen Newell and concludes with a section of fourteen essays devoted to a retrospective on the strength and vision of his work.Sections/Contributors: - Artificial Intelligence in Perspective, D. G. Bobrow.- Foundations. J. McCarthy, R. C. Moore, A. Newell, N. J. Nilsson, J. Gordon and E. H. Shortliffe, J. Pearl, A. K. Mackworth and E. C. Freuder, J. de Kleer.- Vision. H. G. Barrow and J. M. Tenenbaum, B. K. P. Horn and B. Schunck, K. Ikeuchi, T. Kanade.- Qualitative Reasoning. J. de Kleer, K. D. Forbus, B. J. Kuipers, Y. Iwasake and H. A Simon.- Diagnosis. R. Davis, M. R. Genesereth, P. Szolovits and S. G. Pauker, R. Davis, B. G. Buchanan and E. H. Shortliffe, W. J. Clancey.- Architectures. J. S. Aikins, B. Hayes-Roth, M. J. Stefik et al.- Systems. R. E. Fikes and N. J. Nilsson, E. A Feigenbaum and B. G. Buchanan, J. McDermott. Allen Newell. H. A. Simon, M. J. Stefik and S. W. Smoliar, M. A. Arbib, D. C. Dennett, Purves, R. C. Schank and M. Y. Jona, P. S. Rosenbloom and J. E. Laird, P. E. Agre.
