Markov logic : an interface layer for artificial intelligence /
By: Domingos, Pedro.
Contributor(s): Lowd, Daniel.
Material type:
BookSeries: Synthesis lectures on artificial intelligence and machine learning: # 7.Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2009Description: 1 electronic text (viii, 145 p. : ill.) : digital file.ISBN: 9781598296938 (electronic bk.).Uniform titles: Synthesis digital library of engineering and computer science. Subject(s): Artificial intelligence -- Data processing | Interface circuits | Markov processes | Markov logic | Statistical relational learning | Machine learning | Graphical models | Firstorder logic | Probabilistic logic | Markov networks | Markov random fields | Inductive logic programming | Satisfiability | Markov chain Monte Carlo | Belief propagation | Collective classification | Link prediction | Link-based clustering | Entity resolution | Information extraction | Social network analysis | Natural language processing | Robot mapping | Computational biologyDDC classification: 006.3 Online resources: Abstract with links to resource Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
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PK Kelkar Library, IIT Kanpur | Available | EBKE191 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat reader.
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 131-143).
Introduction -- The interface layer -- What is the interface layer for AI -- Markov logic and alchemy: an emerging solution -- Overview of the book -- Markov logic -- First-order logic -- Markov networks -- Markov logic -- Relation to other approaches -- Inference -- Inferring the most probable explanation -- Computing conditional probabilities -- Lazy inference -- Lifted inference -- Learning -- Weight learning -- Structure learning and theory revision -- Unsupervised learning -- Transfer learning -- Extensions -- Continuous domains -- Infinite domains -- Recursive Markov logic -- Relational decision theory -- Applications -- Collective classification -- Social network analysis and link prediction -- Entity resolution -- Information extraction -- Unsupervised coreference resolution -- Robot mapping -- Link-based clustering -- Semantic network extraction from text -- Conclusion -- The alchemy system -- Input files -- Inference -- Weight learning -- Structure learning -- Bibliography -- Biography.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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Most 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.
Also available in print.
Title from PDF t.p. (viewed on July 8, 2009).
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