000 03482nam a22005295i 4500
001 978-3-031-01549-6
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005 20240730163625.0
007 cr nn 008mamaa
008 220601s2009 sz | s |||| 0|eng d
020 _a9783031015496
_9978-3-031-01549-6
024 7 _a10.1007/978-3-031-01549-6
_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 _aDomingos, Pedro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979574
245 1 0 _aMarkov Logic
_h[electronic resource] :
_bAn Interface Layer for Artificial Intelligence /
_cby Pedro Domingos, Daniel Lowd.
250 _a1st ed. 2009.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2009.
300 _aIX, 145 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 _aIntroduction -- Markov Logic -- Inference -- Learning -- Extensions -- Applications -- Conclusion.
520 _aMost subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979575
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 _aLowd, Daniel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979576
710 2 _aSpringerLink (Online service)
_979577
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004216
776 0 8 _iPrinted edition:
_z9783031026775
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979578
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01549-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c84804
_d84804