Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

By Luc De Raedt, Kristian Kersting, and Sriraam Natarajan
Hardcover
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An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Details

Publish date March 24, 2016
Publisher Morgan & Claypool
Format Hardcover
Pages 189
ISBN 9781681732367
168173236X

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