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Statistical Relational Artificial Intelligence

2022-05-31 Computers
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Release : 2022-05-31
Genre : Computers
Kind : eBook
Book Rating : 746/5 ( reviews)

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Book Synopsis Statistical Relational Artificial Intelligence by : Luc De Kang

Download or read book Statistical Relational Artificial Intelligence written by Luc De Kang. This book was released on 2022-05-31. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Relational Artificial Intelligence

2016-03-24 Computers
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Release : 2016-03-24
Genre : Computers
Kind : eBook
Book Rating : 427/5 ( reviews)

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Book Synopsis Statistical Relational Artificial Intelligence by : Luc De Raedt

Download or read book Statistical Relational Artificial Intelligence written by Luc De Raedt. This book was released on 2016-03-24. Available in PDF, EPUB and Kindle. Book excerpt: 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.

An Inductive Logic Programming Approach to Statistical Relational Learning

2006 Computers
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Release : 2006
Genre : Computers
Kind : eBook
Book Rating : 744/5 ( reviews)

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Book Synopsis An Inductive Logic Programming Approach to Statistical Relational Learning by : Kristian Kersting

Download or read book An Inductive Logic Programming Approach to Statistical Relational Learning written by Kristian Kersting. This book was released on 2006. Available in PDF, EPUB and Kindle. Book excerpt: Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.

Introduction to Statistical Relational Learning

2019-09-22 Computers
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Release : 2019-09-22
Genre : Computers
Kind : eBook
Book Rating : 687/5 ( reviews)

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Book Synopsis Introduction to Statistical Relational Learning by : Lise Getoor

Download or read book Introduction to Statistical Relational Learning written by Lise Getoor. This book was released on 2019-09-22. Available in PDF, EPUB and Kindle. Book excerpt: Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.

Boosted Statistical Relational Learners

2015-03-03 Computers
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Release : 2015-03-03
Genre : Computers
Kind : eBook
Book Rating : 445/5 ( reviews)

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Book Synopsis Boosted Statistical Relational Learners by : Sriraam Natarajan

Download or read book Boosted Statistical Relational Learners written by Sriraam Natarajan. This book was released on 2015-03-03. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.

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