Handling Uncertainty in Artificial Intelligence

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.

Demonstrates different numeric and symbolic methods of handling uncertainty in artificial intelligence Highlights on making decisions under uncertain situation Includes examples of real-life uncertain situations which will enhance the understandability of the reader

Autorentext
JYOTISMITA CHAKI, PhD. is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Artificial Intelligence and Machine learning. She has authored and edited many international conferences, journal papers and books. Currently she is the editor of Engineering Applications of Artificial Intelligence Journal, Elsevier, academic editor of PLOS ONE journal and associate editor of Array journal, Elsevier, IET Image Processing, Applied Computational Intelligence and Soft Computing and Machine Learning with Applications journal, Elsevier.

Inhalt

Introduction to handling uncertainty in artificial intelligence.- Probability and Bayesian Theory to Handle Uncertainty in artificial intelligence.- The Dempster-Shafer Theory to handle uncertainty in artificial intelligence.- Certainty factor and evidential reasoning to handle uncertainty in artificial intelligence.- A fuzzy logic-based approach to handle uncertainty in artificial intelligence.- Decision-making under uncertainty in artificial intelligence.- Applications of different methods to handle uncertainty in artificial intelligence.