Markov logic : an interface layer for artificial intelligence
- Responsibility
- Pedro Domingos and Daniel Lowd ; with contributions from Jesse Davis [and others].
- Language
- English. English.
- Imprint
- Cham, Switzerland : Springer, ©2009.
- Physical description
- 1 online resource (viii, 145 pages) : illustrations
- Series
- Synthesis lectures on artificial intelligence and machine learning ; #7.
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Domingos, Pedro.
- Contributor
- Lowd, Daniel.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 131-143).
- Contents
-
- Introduction Markov Logic Inference Learning Extensions Applications Conclusion.
- (source: Nielsen Book Data)
- Publisher's summary
-
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.
(source: Nielsen Book Data)
Subjects
- Subjects
- Artificial intelligence > Mathematics.
- Machine learning.
- First-order logic.
- Markov random fields.
- Probabilities.
- Inference.
- Probability
- Intelligence artificielle > Mathématiques.
- Apprentissage automatique.
- Logique du premier ordre.
- Champs aléatoires de Markov.
- Probabilités.
- Inférence (Logique)
- probability.
- COMPUTERS > Enterprise Applications > Business Intelligence Tools.
- COMPUTERS > Intelligence (AI) & Semantics.
Bibliographic information
- Publication date
- 2009
- Series
- Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; #7
- ISBN
- 9781598296938 (electronic bk.)
- 1598296930 (electronic bk.)
- 9781598296945
- 1598296949
- 9781598296921 (pbk.)
- 1598296922
- 9783031015496 (electronic bk.)
- 3031015495 (electronic bk.)
- DOI
- 10.2200/S00206ED1V01Y200907AIM007
- 10.1007/978-3-031-01549-6
