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Handling Uncertainty in Artificial Intelligence

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Handling Uncertainty in Artificial Intelligence Synopsis

This book demonstrates different methods (as well as real-life examples) of handling uncertainty like probability and Bayesian theory, Dempster-Shafer theory, certainty factor and evidential reasoning, fuzzy logic-based approach, utility theory and expected utility theory. At the end, highlights will be on the use of these methods which can help to make decisions under uncertain situations. This book assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation. The book is comprehensive, but it prohibits unnecessary mathematics.

About This Edition

ISBN: 9789819953325
Publication date:
Author: Jyotismita Chaki
Publisher: Springer an imprint of Springer Nature Singapore
Format: Paperback
Pagination: 101 pages
Series: SpringerBriefs in Applied Sciences and Technology. Computational Intelligence
Genres: Artificial intelligence
Algorithms and data structures