Synopsis
This book provides an up-to-date analysis and description of the three main numerical approaches to modelling approximate reasoning - the probabilistic, possibilistic and the evidential - and presents them in a rigorous yet easy to read manner. Illustrated with many examples, the similarities, differences and limitations of these models are pointed out and a comparative presentation of the main models for representing and managing uncertainty in expert systems given. The book gathers together all the main results recently published and collates in a single volume all the available information that was previously scattered throughout the literature. The text will benefit students and researcher in computer science, artificial intelligence and expert systems.
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