Reasoning with probabilistic and deterministic graphical models: exact algorithms
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 task...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, CA]
Morgan & Claypool
2019
|
| Ausgabe: | second edition |
| Schriftenreihe: | Synthesis lectures on artificial intelligence and machine learning
#41 |
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | xiv, 185 Seiten Diagramme (teilweise farbig) |
| ISBN: | 9781681734903 |
Internformat
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| 520 | |a 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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Datensatz im Suchindex
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|---|---|
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| author | Dechter, Rina 1950- |
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| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| edition | second edition |
| format | Book |
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| id | DE-604.BV046741527 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:39:30Z |
| indexdate | 2024-07-10T08:52:33Z |
| institution | BVB |
| isbn | 9781681734903 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032151432 |
| oclc_num | 1176437643 |
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| owner_facet | DE-11 |
| physical | xiv, 185 Seiten Diagramme (teilweise farbig) |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Morgan & Claypool |
| record_format | marc |
| series | Synthesis lectures on artificial intelligence and machine learning |
| series2 | Synthesis lectures on artificial intelligence and machine learning |
| spelling | Dechter, Rina 1950- Verfasser (DE-588)174090005 aut Reasoning with probabilistic and deterministic graphical models exact algorithms Rina Dechter, University of California, Irvine second edition [San Rafael, CA] Morgan & Claypool 2019 © 2019 xiv, 185 Seiten Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Synthesis lectures on artificial intelligence and machine learning #41 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 Graphical modeling (Statistics) Bayesian statistical decision theory Reasoning Algorithms Machine learning Erscheint auch als Online-Ausgabe 978-1-68173-491-0 Synthesis lectures on artificial intelligence and machine learning #41 (DE-604)BV035750800 41 |
| spellingShingle | Dechter, Rina 1950- Reasoning with probabilistic and deterministic graphical models exact algorithms Synthesis lectures on artificial intelligence and machine learning Graphical modeling (Statistics) Bayesian statistical decision theory Reasoning Algorithms Machine learning |
| title | Reasoning with probabilistic and deterministic graphical models exact algorithms |
| title_auth | Reasoning with probabilistic and deterministic graphical models exact algorithms |
| title_exact_search | Reasoning with probabilistic and deterministic graphical models exact algorithms |
| title_exact_search_txtP | Reasoning with probabilistic and deterministic graphical models exact algorithms |
| title_full | Reasoning with probabilistic and deterministic graphical models exact algorithms Rina Dechter, University of California, Irvine |
| title_fullStr | Reasoning with probabilistic and deterministic graphical models exact algorithms Rina Dechter, University of California, Irvine |
| title_full_unstemmed | Reasoning with probabilistic and deterministic graphical models exact algorithms Rina Dechter, University of California, Irvine |
| title_short | Reasoning with probabilistic and deterministic graphical models |
| title_sort | reasoning with probabilistic and deterministic graphical models exact algorithms |
| title_sub | exact algorithms |
| topic | Graphical modeling (Statistics) Bayesian statistical decision theory Reasoning Algorithms Machine learning |
| topic_facet | Graphical modeling (Statistics) Bayesian statistical decision theory Reasoning Algorithms Machine learning |
| volume_link | (DE-604)BV035750800 |
| work_keys_str_mv | AT dechterrina reasoningwithprobabilisticanddeterministicgraphicalmodelsexactalgorithms |