000 06996nam a2200805 i 4500
001 7446017
003 IEEE
005 20200413152921.0
006 m eo d
007 cr cn |||m|||a
008 160414s2016 caua foab 001 0 eng d
020 _a9781627058421
_qebook
020 _z9781627058414
_qprint
024 7 _a10.2200/S00692ED1V01Y201601AIM032
_2doi
035 _a(CaBNVSL)swl00406397
035 _a(OCoLC)946774679
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQ336
_b.R247 2016
082 0 4 _a006.3
_223
100 1 _aRaedt, Luc de,
_d1964-,
_eauthor.
245 1 0 _aStatistical relational artificial intelligence :
_blogic, probability, and computation /
_cLuc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole.
264 1 _aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_c2016.
300 _a1 PDF (xiv, 175 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on artificial intelligence and machine learning,
_x1939-4616 ;
_v# 32
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 139-167) and index.
505 0 _a1. Motivation -- 1.1 Uncertainty in complex worlds -- 1.2 Challenges of understanding StarAI -- 1.3 The benefits of mastering StarAI -- 1.4 Applications of StarAI -- 1.5 Brief historical overview --
505 8 _aPart I. Representations -- 2. Statistical and relational AI representations -- 2.1 Probabilistic graphical models -- 2.1.1 Bayesian networks -- 2.1.2 Markov networks and factor graphs -- 2.2 First-order logic and logic programming --
505 8 _a3. Relational probabilistic representations -- 3.1 A general view: parameterized probabilistic models -- 3.2 Two example representations: Markov logic and ProbLog -- 3.2.1 Undirected relational model: Markov logic -- 3.2.2 Directed relational models: ProbLog --
505 8 _a4. Representational issues -- 4.1 Knowledge representation formalisms -- 4.2 Objectives for representation language -- 4.3 Directed vs. undirected models -- 4.4 First-order logic vs. logic programs -- 4.5 Factors and formulae -- 4.6 Parameterizing atoms -- 4.7 Aggregators and combining rules -- 4.8 Open universe models -- 4.8.1 Identity uncertainty -- 4.8.2 Existence uncertainty -- 4.8.3 Ontologies --
505 8 _aPart II. Inference -- 5. Inference in propositional models -- 5.1 Probabilistic inference -- 5.1.1 Variable elimination -- 5.1.2 Recursive conditioning -- 5.1.3 Belief propagation -- 5.2 Logical inference -- 5.2.1 Propositional logic, satisfiability, and weighted model counting -- 5.2.2 Semiring inference -- 5.2.3 The least Herbrand model -- 5.2.4 Grounding -- 5.2.5 Proving --
505 8 _a6. Inference in relational probabilistic models -- 6.1 Grounded inference for relational probabilistic models -- 6.1.1 Weighted model counting -- 6.1.2 WMC for Markov logic -- 6.1.3 WMC for ProbLog -- 6.1.4 Knowledge compilation -- 6.2 Lifted inference: exploiting symmetries -- 6.2.1 Exact lifted inference -- 6.3 (Lifted) approximate inference --
505 8 _aPart III. Learning -- 7. Learning probabilistic and logical models -- 7.1 Learning probabilistic models -- 7.1.1 Fully observed data and known structure -- 7.1.2 Partially observed data with known structure -- 7.1.3 Unknown structure and parameters -- 7.2 Logical and relational learning -- 7.2.1 Two learning settings -- 7.2.2 The search space -- 7.2.3 Two algorithms: clausal discovery and FOIL -- 7.2.4 From propositional to first-order logic -- 7.2.5 An ILP example --
505 8 _a8. Learning probabilistic relational models -- 8.1 Learning as inference -- 8.2 The learning problem -- 8.2.1 The data used -- 8.3 Parameter learning of relational models -- 8.3.1 Fully observable data -- 8.3.2 Partially observed data -- 8.3.3 Learning with latent variables -- 8.4 Structure learning of probabilistic relational models -- 8.4.1 A vanilla structure learning approach -- 8.4.2 Probabilistic relational models -- 8.4.3 Boosting -- 8.5 Bayesian learning -- Part IV. Beyond probabilities --
505 8 _a9. Beyond basic probabilistic inference and learning -- 9.1 Lifted satisfiability -- 9.2 Acting in noisy relational worlds -- 9.3 Relational optimization --
505 8 _a10. Conclusions -- Bibliography -- Authors' biographies -- Index.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _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.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on April 14, 2016).
650 0 _aArtificial intelligence
_xComputer simulation.
650 0 _aLogic
_xComputer simulation.
653 _aprobabilistic logic models
653 _arelational probabilistic models
653 _alifted inference
653 _astatistical relational learning
653 _aprobabilistic programming
653 _ainductive logic programming
653 _alogic programming
653 _amachine learning
653 _aProlog
653 _aProblog
653 _aMarkov logic networks
700 1 _aKersting, Kristian.,
_eauthor.
700 1 _aNatarajan, Sriraam.,
_eauthor.
700 1 _aPoole, David L.
_q(David Lynton),
_d1958-,
_eauthor.
776 0 8 _iPrint version:
_z9781627058414
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on artificial intelligence and machine learning ;
_v# 32.
_x1939-4616
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7446017
999 _c562198
_d562198