- Tapa blanda: 192 páginas
- Editor: Morgan & Claypool Publishers (1 de diciembre de 2013)
- Colección: Synthesis Lectures on Artificial Intelligence and Machine Learning
- Idioma: Inglés
- ISBN-10: 162705197X
- ISBN-13: 978-1627051972
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
- Clasificación en los más vendidos de Amazon: nº51.498 en Libros en idiomas extranjeros (Ver el Top 100 en Libros en idiomas extranjeros)
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Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning) (Inglés) Tapa blanda – 1 dic 2013
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Reseña del editor
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.
Biografía del autor
Rina Dechter research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing, and probabilistic reasoning. She is a professor of computer science at the University of California, Irvine. She holds a Ph.D. from UCLA, an M.S. degree in applied mathematics from the Weizmann Institute, and a B.S. in mathematics and statistics from the Hebrew University in Jerusalem. She is an author of Constraint Processing published by Morgan Kaufmann (2003), has co-authored over 150 research papers, and has served on the editorial boards of: Artificial Intelligence, the Constraint Journal, Journal of Artificial Intelligence Research (JAIR), and Journal of Machine Learning Research (JMLR). She is a fellow of the American Association of Artificial Intelligence, was a Radcliffe Fellow 2005-2006, received the 2007 Association of Constraint Programming (ACP) Research Excellence Award, and she is a 2013 ACM Fellow. She has been Co-Editor-in-Chief of Artificial Intelligence since 2011. She is also co-editor with Hector Geffner and Joe Halpern of the book Heuristics, Probability and Causality: A Tribute to Judea Pearl, College Publications, 2010.
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This book is a concise, yet deep, overview of many methods for many different problems seen from a graphical models point of view (deterministic constraint processing, probabilistic inference in Bayesian and Markov networks, cost networks, mixed networks, MAP, MPE). It starts by presenting a unified view of them all, and proceeds by discussing multiple methods, from simpler to more sophisticated, in a gradual and natural sequence that is easy to understand. It covers inference methods such as belief propagation, variable elimination, junction tree, and search methods (OR search and then AND-OR search). At the end it discusses issues in integrating these methods, and how they relate to each other. After reading the book I had much better idea of where all these elements fit within this landscape.