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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

Kersting, Kristian
Natarajan, Sriraam
de Raedt, Luc

Hardcover

Series: Synthesis Lectures on Artificial Intelligence and Machine Le

General ComputersGeneral MathematicsProbability & Statistics

ISBN10: 3031000226
ISBN13: 9783031000225
Publisher: Springer
Published: Mar 24 2016
Pages: 175
Weight: 1.21
Height: 0.50 Width: 7.50 Depth: 9.25
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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