UNIFYING LOGICAL AND STATISTICAL AI

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.

Bio: I received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon. I received an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. I spent two years as an assistant professor at IST, before joining the faculty of the University of Washington in 1999. I'm the author or co-author of over 100 technical publications in machine learning, data mining, and other areas. I'm a member of the editorial board of the Machine Learning journal and the advisory board of JAIR, and a co-founder of the International Machine Learning Society. I was program co-chair of KDD-2003, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, SIGMOD, WWW, and others. I've received a Sloan Fellowship, an NSF CAREER Award, a Fulbright Scholarship, an IBM Faculty Award, two KDD best paper awards, and other distinctions.