Rina (University of California Dechter, Irvine)'s Books

Constraint Processing

By: Rina Dechter

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Constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. In Constraint Processing, Rina Dechter synthesizes these contributions, as well as her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms.

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504

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Morgan Kaufmann

Published Date

ISBN 10

1558608907

ISBN 13

9781558608900

Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms, Second Edition

By: Rina Dechter

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Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms, Second Edition

By: Rina Dechter

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

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Page Count

201

Publisher

Morgan & Claypool Publishers

Published Date

ISBN 10

1681734915

ISBN 13

9781681734910

Probabilistic and Causal Inference

The Works of Judea Pearl

By: Hector Geffner,Rina Dechter,Joseph Halpern

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Probabilistic and Causal Inference

The Works of Judea Pearl

By: Hector Geffner,Rina Dechter,Joseph Halpern

Professor Judea Pearl won the 2011 Turing Award “for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.” This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988–2001), and causality, recent period (2002–2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl’s work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

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Page Count

946

Publisher

Morgan & Claypool

Published Date

ISBN 10

1450395899

ISBN 13

9781450395892

Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms

By: Rina Kraus

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Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms

By: Rina Kraus

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

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Page Count

187

Publisher

Springer Nature

Published Date

ISBN 10

3031015665

ISBN 13

9783031015663

Constraint Processing

By: Rina Dechter

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Constraint Processing

By: Rina Dechter

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504

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Morgan Kaufmann

Published Date

ISBN 10

1558608907

ISBN 13

9781558608900

Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms, Second Edition

By: Rina Sreedharan

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Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms, Second Edition

By: Rina Sreedharan

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Page Count

185

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Springer Nature

Published Date

ISBN 10

3031015835

ISBN 13

9783031015830

Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms

By: Rina Dechter

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Reasoning with Probabilistic and Deterministic Graphical Models

Exact Algorithms

By: Rina Dechter

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

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Page Count

193

Publisher

Morgan & Claypool Publishers

Published Date

ISBN 10

1627051988

ISBN 13

9781627051989

Book's by Rina (University of California Dechter, Irvine)

Constraint Processing

Constraint Processing

Reasoning with Probabilistic and Deterministic Graphical Models

Reasoning with Probabilistic and Deterministic Graphical Models

Probabilistic and Causal Inference

Probabilistic and Causal Inference

Reasoning with Probabilistic and Deterministic Graphical Models

Reasoning with Probabilistic and Deterministic Graphical Models

Constraint Processing

Constraint Processing

Reasoning with Probabilistic and Deterministic Graphical Models

Reasoning with Probabilistic and Deterministic Graphical Models

Reasoning with Probabilistic and Deterministic Graphical Models

Reasoning with Probabilistic and Deterministic Graphical Models

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