Statistical Relational Artificial Intelligence
Logic, Probability, and Computation
Seiten
2016
Morgan & Claypool Publishers (Verlag)
978-1-68173-236-7 (ISBN)
Morgan & Claypool Publishers (Verlag)
978-1-68173-236-7 (ISBN)
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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.
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.
Luc De Raedt, KU Leuven, Belgium. Kristian Kersting Technical University of Dortmund, Germany. Sriraam Natarajan, Indiana University, USA. David Poole, University of British Columbia, Canada.
Preface
Motivation
Statistical and Relational AI Representations
Relational Probabilistic Representations
Representational Issues
Inference in Propositional Models
Inference in Relational Probabilistic Models
Learning Probabilistic and Logical Models
Learning Probabilistic Relational Models
Beyond Basic Probabilistic Inference and Learning
Conclusions
Bibliography
Authors' Biographies
Index
| Erscheinungsdatum | 19.11.2017 |
|---|---|
| Reihe/Serie | Synthesis Lectures on Artificial Intelligence and Machine Learning |
| Verlagsort | San Rafael |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Gewicht | 555 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-68173-236-X / 168173236X |
| ISBN-13 | 978-1-68173-236-7 / 9781681732367 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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