000			03508nam a2200457 i 4500
999			_c386938 _d386938
001			386938
003			ES-MaUEC
005			20240111050230.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2016 sz | f |||| 0|eng d
020			_a9783031015748
024	7		_a10.1007/978-3-031-01574-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ336 _b2016 EB
100	1		_aRaedt, Luc de _d1964- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686154
245	1	0	_aStatistical Relational Artificial Intelligence : _bLogic, Probability, and Computation _cby Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XIV, 175 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2016
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aKersting, Kristian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _999125
700	1		_aNatarajan, Sriraam _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686155
700	1		_aPoole, David L. _q(David Lynton) _d1958- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686156
776	0	8	_iPrinted edition: _z9783031000225
776	0	8	_iPrinted edition: _z9783031004469
776	0	8	_iPrinted edition: _z9783031027024
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01574-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI