Artificial intelligence systems must be able to learn, reason logically, and handle uncertainty. Research has focused on each of these goals individually, and only recently have attempts been made to achieve all three at once.   In this colloquia, Pedro Domingos, UW Computer Science & Engineering, describes Markov logic: a representation that combines the full power of first-order logic and probabilistic graphical models, and algorithms for learning and inference in it. Experiments in a real-world university domain illustrate the promise of this approach. From the Series:CSE Colloquia - 2005