Statistical Relational Artificial Intelligence

Logic, Probability, and Computation

Nonfiction, Computers, Advanced Computing, Theory, Artificial Intelligence, General Computing
Cover of the book Statistical Relational Artificial Intelligence by Kristian Kersting, Luc De Raedt, Sriraam Natarajan, David Poole, Morgan & Claypool Publishers
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Author: Kristian Kersting, Luc De Raedt, Sriraam Natarajan, David Poole ISBN: 9781681731803
Publisher: Morgan & Claypool Publishers Publication: March 24, 2016
Imprint: Morgan & Claypool Publishers Language: English
Author: Kristian Kersting, Luc De Raedt, Sriraam Natarajan, David Poole
ISBN: 9781681731803
Publisher: Morgan & Claypool Publishers
Publication: March 24, 2016
Imprint: Morgan & Claypool Publishers
Language: English

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.

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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.

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