AI After Dark: Computers Playing Poker

The game of poker presents a serious challenge for artificial
intelligence. The game is essentially about dealing with many forms of
uncertainty: unobservable opponent cards, undetermined future cards,
and unknown opponent strategies. Coping with these uncertainties is
critical to playing at a high-level.  In July 2008, the University of
Alberta's poker playing program, Polaris, became the first to defeat
top professional players at any variant of poker in a meaningful
competition. In this talk, I'll tell the story of this match
interleaved with the science that enabled Polaris's accomplishment.

Short Bio:

Michael Bowling is an associate professor at the University of
Alberta.  He received his Ph.D. in 2003 from Carnegie Mellon
University in the area of artificial intelligence.  His research
focuses on machine learning, game theory, and robotics, and he is
particularly fascinated by the problem of how computers can learn to
play games through experience.