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 belief propagation, Markov chain Monte Carlo, resolution, and satisfiability. Learning algorithms are based on inductive logic programming and convex optimization. Markov logic has been successfully applied to problems in information extraction, natural language processing, social network analysis, robot mapping, bioinformatics and others, and is the basis of the open-source Alchemy system. Joint work with Jesse Davis, Stanley Kok, Daniel Lowd, Aniruddh Nath, Hoifung Poon, Matt Richardson, Parag Singla, Marc Sumner, and Jue Wang.
Unifying Logical and Statistical AI Monday 23 March 2009, 4.00pm