Description
Morgan Statistical Relational Artificial Intelligence Logic Probability and Computation by Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole
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._x000D__x000D_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._x000D__x000D_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._x000D_ Table of contents :- _x000D_
Preface_x000D_
Motivation_x000D_
Statistical and Relational AI Representations_x000D_
Relational Probabilistic Representations_x000D_
Representational Issues_x000D_
Inference in Propositional Models_x000D_
Inference in Relational Probabilistic Models_x000D_
Learning Probabilistic and Logical Models_x000D_
Learning Probabilistic Relational Models_x000D_
Beyond Basic Probabilistic Inference and Learning_x000D_
Conclusions_x000D_
Bibliography_x000D_
Authors' Biographies_x000D_
Index_x000D_