TY - JOUR
AU - Zadeh, Lotfi A.
AB - The seminal work of Glenn Shafer—which is based on an earlier work of Arthur Dempster—was published at a time when the theory of expert systems was in its infancy and there was little interest within the AI community in issues relating to probabilistic or evidential reasoning.Recognition of the relevance of the Dempster‐Shafer theory to the management of uncertainty in expert systems was slow in coming. Today, it is the center of considerable attention within AI due in large measure to (a) the emergence of expert systems as one of the most significant areas of activity in knowledge engineering, and (b) the important extensions, applications and implementations of Shafer's theory made by John Lowrance at SRI International, Jeff Barnett at USC/ISI, and Ted Shortliffe and Jean Gordon at Stanford University.What are the basic ideas behind the Dempster‐Shafer theory? In what ways is it relevant to expert systems? What are its potentialities and limitations? My review of Shafer's book will be more of an attempt to provide some answers to these and related questions than a chapter‐by‐chapter analysis of its contents.To understand Shafer's theory, it is best to start with a careful reading of Dempster's original paper (Dempster, 1967) which provides 
TI - A Mathematical Theory of Evidence. Glenn Shafer. Princeton University Press, Princeton, NJ, 1976.
JF - AI Magazine
DO - 10.1609/aimag.v5i3.452
DA - 1984-09-01
UR - https://www.deepdyve.com/lp/wiley/a-mathematical-theory-of-evidence-glenn-shafer-princeton-university-9U6WQbjTUR
SP - 81
EP - 83
VL - 5
IS - 3
DP - DeepDyve
ER -