Abstract: Just a few years ago general automated inference beyond hundred variable problems appeared to be out of practical reach. Current reasoning engines can handle problems with over a million variables and several millions of constraints. I will discuss what led to such a dramatic scale-up, and how progress in reasoning technology has opened up a range of new applications in artificial intelligence and computer science in general. I will also describe current research directions centered around the integration of probabilistic and logical inference, the characterization of combinatorial search spaces using methods from statistical physics, the discovery of hidden problem structure using sampling techniques and other ideas from machine learning, and the development of inference methods for multi-agent systems.

This is joint work with Carla Gomes.

Refreshments at 4:30 in 657 Rhodes Hall.