Unifying Logical and Statistical AI
Pedro Domingos
,
Stanley Kok
,
Hoifung Poon,
Matthew Richardson
and
Parag Singla
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.
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