000 03798nam a22005655i 4500
001 978-3-031-01574-8
003 DE-He213
005 20240730163428.0
007 cr nn 008mamaa
008 220601s2016 sz | s |||| 0|eng d
020 _a9783031015748
_9978-3-031-01574-8
024 7 _a10.1007/978-3-031-01574-8
_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 _aDe Raedt, Luc.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978456
245 1 0 _aStatistical Relational Artificial Intelligence
_h[electronic resource] :
_bLogic, Probability, and Computation /
_cby Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXIV, 175 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 -- Motivation -- Statistical and Relational AI Representations -- Relational Probabilistic Representations -- Representational Issues -- Inference in Propositional Models -- Inference in Relational Probabilistic Models -- Learning Probabilistic and Logical Models -- Learning Probabilistic Relational Models -- Beyond Basic Probabilistic Inference and Learning -- Conclusions -- Bibliography -- Authors' Biographies -- Index.
520 _aAn intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_978457
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aKersting, Kristian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978458
700 1 _aNatarajan, Sriraam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978459
700 1 _aPoole, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978460
710 2 _aSpringerLink (Online service)
_978461
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000225
776 0 8 _iPrinted edition:
_z9783031004469
776 0 8 _iPrinted edition:
_z9783031027024
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_978462
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01574-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c84594
_d84594