Overview
- Authors:
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Paul Krause
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Imperial Cancer Reserch Fund, London, UK
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Dominic Clark
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Imperial Cancer Reserch Fund, London, UK
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Table of contents (8 chapters)
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- Paul Krause, Dominic Clark
Pages 1-15
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- Paul Krause, Dominic Clark
Pages 16-51
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- Paul Krause, Dominic Clark
Pages 52-67
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- Paul Krause, Dominic Clark
Pages 68-103
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- Paul Krause, Dominic Clark
Pages 104-142
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- Paul Krause, Dominic Clark
Pages 143-196
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- Paul Krause, Dominic Clark
Pages 197-235
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- Paul Krause, Dominic Clark
Pages 236-251
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Back Matter
Pages 252-277
About this book
The representation of uncertainty is a central issue in Artificial Intelligence (AI) and is being addressed in many different ways. Each approach has its proponents, and each has had its detractors. However, there is now an in creasing move towards the belief that an eclectic approach is required to represent and reason under the many facets of uncertainty. We believe that the time is ripe for a wide ranging, yet accessible, survey of the main for malisms. In this book, we offer a broad perspective on uncertainty and approach es to managing uncertainty. Rather than provide a daunting mass of techni cal detail, we have focused on the foundations and intuitions behind the various schools. The aim has been to present in one volume an overview of the major issues and decisions to be made in representing uncertain knowl edge. We identify the central role of managing uncertainty to AI and Expert Systems, and provide a comprehensive introduction to the different aspects of uncertainty. We then describe the rationales, advantages and limitations of the major approaches that have been taken, using illustrative examples. The book ends with a review of the lessons learned and current research di rections in the field. The intended readership will include researchers and practitioners in volved in the design and implementation of Decision Support Systems, Ex pert Systems, other Knowledge-Based Systems and in Cognitive Science.
