2006 UnifyingLogicalandStatisticalAI

• (Domingos et al., 2006) ⇒ Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew Richardson, and Parag Singla. (2006). “Unifying Logical and Statistical AI.” In: Proceedings of the 21st national conference on Artificial intelligence (AAAI 2006).

Subject Headings: Markov Logic Network.

Cited By

• http://scholar.google.com/scholar?q=%222006%22+Unifying+Logical+and+Statistical+AI
• http://dl.acm.org/citation.cfm?id=1597538.1597540&preflayout=flat#citedby

Quotes

Abstract

Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to problems in entity resolution, link prediction, information extraction and others, and is the basis of the open-source Alchemy system.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 UnifyingLogicalandStatisticalAI	Stanley Kok Pedro Domingos Hoifung Poon Matthew Richardson Parag Singla			Unifying Logical and Statistical AI						2006