Unifying logical and statistical AI

Authors:
Pedro Domingos;Stanley Kok;Hoifung Poon;Matthew Richardson;Parag Singla
Affiliations:
Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA
Venue:
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Year:
2006

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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.