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© 2008

Classic Works of the Dempster-Shafer Theory of Belief Functions

  • Roland R. Yager
  • Liping Liu

Benefits

  • Collects the key original contributions that are widely recognized in the field of Dempster-Shafer Theory of Belief functions

  • Authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management

Book

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 219)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Arthur P. Dempster
    Pages 73-104
  3. Hung T. Nguyen
    Pages 105-116
  4. Glenn Shafer
    Pages 183-196
  5. Glenn Shafer
    Pages 217-264
  6. Glenn Shafer
    Pages 265-290
  7. Glenn Shafer, Amos Tversky
    Pages 345-374
  8. Didier Dubois, Henri Prade
    Pages 375-410
  9. John D. Lowrance, Thomas D. Garvey, Thomas M. Strat
    Pages 419-434
  10. Glenn Shafer, Roger Logan
    Pages 449-476

About this book

Introduction

This book brings together a collection of classic research papers on the Dempster-Shafer theory of belief functions. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in expert systems. This book will serve as the authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management. From over 120 nominated contributions, the editors selected 30 papers, which are widely regarded as classics and will continue to make impacts on the future development of the field. The contributions are grouped into seven sections, including conceptual foundations, theoretical perspectives, theoretical extensions, alternative interpretations, and applications to artificial intelligence, decision-making, and statistical inferences. The book also includes a foreword by Dempster and Shafer reflecting the development of the theory in the last forty years, and an introduction describing the basic elements of the theory and how each paper contributes to the field.

Keywords

Bayesian inference Mathematica artificial intelligence decision making decision theory economics expert system fuzzy fuzzy logic fuzzy set fuzzy sets intelligence probability statistics uncertainty

Editors and affiliations

  • Roland R. Yager
    • 1
  • Liping Liu
    • 2
  1. 1.Machine Intelligence InstituteIona CollegeNew RochelleUSA
  2. 2.Department of Management and Information SystemsUniversity of Akron College of Business Administration351 AkronUSA

Bibliographic information

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