Handling Uncertainty in Artificial Intelligence
Chaki, Jyotismita.
Handling Uncertainty in Artificial Intelligence [electronic resource] / by Jyotismita Chaki. - 1st ed. 2023. - XIII, 101 p. 42 illus., 2 illus. in color. online resource. - SpringerBriefs in Computational Intelligence, 2625-3712 . - SpringerBriefs in Computational Intelligence, .
Acceso multiusuario
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.
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.
9789819953332
Computational intelligence.
Artificial intelligence.
Algorithms.
Computational Intelligence.
Artificial Intelligence.
Algorithms.
Q342
006.3
Handling Uncertainty in Artificial Intelligence [electronic resource] / by Jyotismita Chaki. - 1st ed. 2023. - XIII, 101 p. 42 illus., 2 illus. in color. online resource. - SpringerBriefs in Computational Intelligence, 2625-3712 . - SpringerBriefs in Computational Intelligence, .
Acceso multiusuario
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.
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.
9789819953332
Computational intelligence.
Artificial intelligence.
Algorithms.
Computational Intelligence.
Artificial Intelligence.
Algorithms.
Q342
006.3
