Markov Logic: An Interface Layer for Artificial Intelligence

DOI10.2200/S00206ED1V01Y200907AIM007zbMath1202.68403OpenAlexW1997945384MaRDI QIDQ3575744

Daniel Lowd, Pedro Domingos

Publication date: 28 July 2010

Published in: Synthesis Lectures on Artificial Intelligence and Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2200/s00206ed1v01y200907aim007

zbMATH Keywords

Markov chain Monte Carlofirst-order logicartificial intelligencereasoningsatisfiabilityinductive logic programmingmachine learningprobabilistic graphical modelslink predictionsocial network analysisbelief propagationentity resolutioninformation extractionMarkov networksprobabilistic logicrobot mappingMarkov logicAlchemycollective classificationlink-based clustering

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic in artificial intelligence (68T27) Research exposition (monographs, survey articles) pertaining to computer science (68-02)

Related Items (23)

Distributional logic programming for Bayesian knowledge representation ⋮ Fast learning of relational dependency networks ⋮ Uncertain logic processing: logic-based inference and reasoning using Dempster-Shafer models ⋮ PR-OWL - a language for defining probabilistic ontologies ⋮ Probabilistic Event Calculus for Event Recognition ⋮ Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases ⋮ Logic explained networks ⋮ Gradient-based boosting for statistical relational learning: the relational dependency network case ⋮ Some thoughts on knowledge-enhanced machine learning ⋮ Structure learning for relational logistic regression: an ensemble approach ⋮ Neural probabilistic logic programming in DeepProbLog ⋮ An overview of algorithmic approaches to compute optimum entropy distributions in the expert system shell MECore (extended version) ⋮ Model-based exception mining for object-relational data ⋮ Scalable learning and inference in Markov logic networks ⋮ Uncertainty Quantification for Markov Random Fields ⋮ Modelling relational statistics with Bayes nets ⋮ A general approach to reasoning with probabilities ⋮ Probabilistic sentence satisfiability: an approach to PSAT ⋮ Regression learning based on incomplete relationships between attributes ⋮ Unnamed Item ⋮ A taxonomy of weight learning methods for statistical relational learning ⋮ Lifted graphical models: a survey ⋮ Probabilistic Reasoning in the Description Logic $$\mathcal {ALCP}$$ with the Principle of Maximum Entropy

This page was built for publication: Markov Logic: An Interface Layer for Artificial Intelligence