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001 978-3-031-01566-3
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020 _a9783031015663
_9978-3-031-01566-3
024 7 _a10.1007/978-3-031-01566-3
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aKraus, Rina.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979942
245 1 0 _aReasoning with Probabilistic and Deterministic Graphical Models
_h[electronic resource] :
_bExact Algorithms /
_cby Rina Kraus.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aIV, 191 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Introduction -- What are Graphical Models -- Inference: Bucket Elimination for Deterministic Networks -- Inference: Bucket Elimination for Probabilistic Networks -- Tree-Clustering Schemes -- AND/OR Search Spaces and Algorithms for Graphical Models -- Combining Search and Inference: Trading Space for Time -- Conclusion -- Bibliography -- Author's Biography .
520 _aGraphical 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979943
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
710 2 _aSpringerLink (Online service)
_979944
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004384
776 0 8 _iPrinted edition:
_z9783031026942
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979945
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01566-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c84874
_d84874