Unifying logical and statistical AI Export

@inproceedings{citeulike:5899508, 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.}, author = {Domingos, Pedro and Kok, Stanley and Poon, Hoifung and Richardson, Matthew and Singla, Parag}, booktitle = {AAAI'06: Proceedings of the 21st national conference on Artificial intelligence}, citeulike-article-id = {5899508}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1597540}, isbn = {978-1-57735-281-5}, keywords = {alchemy, markov\_logic\_networks, mln}, location = {Boston, Massachusetts}, pages = {2--7}, posted-at = {2009-10-06 17:46:03}, priority = {2}, publisher = {AAAI Press}, title = {Unifying logical and statistical AI}, url = {http://portal.acm.org/citation.cfm?id=1597540}, year = {2006} }

TY - CONF ID - citeulike:5899508 L3 - citeulike-article-id:5899508 N2 - 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. CY - Boston, Massachusetts SN - 978-1-57735-281-5 EP - 7 TI - Unifying logical and statistical AI PB - AAAI Press T2 - AAAI'06: Proceedings of the 21st national conference on Artificial intelligence SP - 2 KW - alchemy KW - markov_logic_networks KW - mln AU - Domingos, Pedro AU - Kok, Stanley AU - Poon, Hoifung AU - Richardson, Matthew AU - Singla, Parag PY - 2006/// UR - http://portal.acm.org/citation.cfm?id=1597540 ER -