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Zustand: New. KlappentextrnrnThe field of Artificial Intelligence has changed a great deal since the 80s, and arguably no one has played a larger role in that change than Judea Pearl. Judea Pearl s work made probability .

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Taschenbuch. Zustand: Neu. Neuware - The field of Artificial Intelligence has changed a great deal since the 80s, and arguably no one has played a larger role in that change than Judea Pearl. Judea Pearl's work made probability the prevailing language of modern AI and, perhaps more significantly, it placed the elaboration of crisp and meaningful models, and of effective computational mechanisms, at the center of AI research. This book is a collection of articles in honor of Judea Pearl, written by close colleagues and former students. Its three main parts, heuristics, probabilistic reasoning, and causality, correspond to the titles of the three ground-breaking books authored by Judea, and are followed by a section of short reminiscences. In this volume, leading authors look at the state of the art in the fields of heuristic, probabilistic, and causal reasoning, in light of Judea's seminal contributors. The authors list include Blai Bonet, Eric Hansen, Robert Holte, Jonathan Schaeffer, Ariel Felner, Richard Korf, Austin Parker, Dana Nau, V. S. Subrahmanian, Hector Geffner, Ira Pohl, Adnan Darwiche, Thomas Dean, Rina Dechter, Bozhena Bidyuk, Robert Matescu, Emma Rollon, Michael I. Jordan, Michael Kearns, Daphne Koller, Brian Milch, Stuart Russell, Azaria Paz, David Poole, Ingrid Zukerman, Carlos Brito, Philip Dawid, Felix Elwert, Christopher Winship, Michael Gelfond, Nelson Rushton, Moises Goldszmidt, Sander Greenland, Joseph Y. Halpern, Christopher Hitchcock, David Heckerman, Ross Shachter, Vladimir Lifschitz, Thomas Richardson, James Robins, Yoav Shoham, Peter Spirtes, Clark Glymour, Richard Scheines, Robert Tillman, Wolfgang Spohn, Jian Tian, Ilya Shpitser, Nils Nilsson, Edward T. Purcell, and David Spiegelhalter.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Hardback. Zustand: New. Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of 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. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning. In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.…

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paperback. Zustand: Gut. 192 Seiten; 9781627051972.3 Gewicht in Gramm: 500.

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4°, XII, 565 Seiten. Illustrierter Orig.Karton. = Tributes; Vol. 11. Erste Ausgabe. Mit Fußnoten und Literaturangaben. - Einband leicht bestoßen. Gutes Exemplar.

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Taschenbuch. Zustand: Neu. Reasoning with Probabilistic and Deterministic Graphical Models | Exact Algorithms, Second Edition | Rina Dechter | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xiv | Englisch | 2019 | Springer | EAN 9783031004551 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…