It's by Stuart Russell's group at Berkeley (he coauthored the AI bible with Peter Norvig). Their tutorial: http://bayesianlogic.github.io/download/BLOG-tutorial

It's by Stuart Russell's group at Berkeley (he coauthored the AI bible with Peter Norvig). Their tutorial: http://bayesianlogic.github.io/download/BLOG-tutorial-2014.pdf http://bayesianlogic.github.io/download/BLOG-tutorial-2014.p... Page 58 has some good sample code. Semantics are on page 70. Every well-formed BLOG model specifies a unique proper probability distribution over all possible worlds definable given its vocabulary •No infinite receding ancestor chains; •no conditioned cycles; •all expressions finitely evaluable; •Functions of countable sets They instantiate some parts of the network and do inference with MCMC. I wonder how it compares to the Markov Logic approach from the University of Washington.