Multiagent learning and planning, game theory, reinforcement learning, commercial computer games, robotics.
My research focuses on machine learning, games, and robotics, and I'm particularly fascinated by the problem of how computers can learn to play games through experience.
I am the leader of the Computer Poker Research Group, which has built some of the best poker playing programs on the planet. The programs were the first to beat top professional players in a meaningful competition and more recently have been shown to play a nearly perfect game of limit Texas hold'em poker. I also started the Arcade Learning Environment, a research testbed for investigating artificial intelligence techniques capable of general competency using Atari 2600 games. More recently I have begun exploring the application of advanced analytics to the sport of curling.
I completed my Ph.D. at Carnegie Mellon University in 2003, where my dissertation was focused on multiagent learning and I was extensively involved in the RoboCup robot soccer initiative. My research has been featured on the television programs Scientific American Frontiers, National Geographic Today, and Discovery Channel Canada, as well appearing in the New York Times, Wired, on CBC and BBC radio, and twice in exhibits at the Smithsonian Museums in Washington, DC.
Team-based exploration of the formal elements of games including tabletop games, sports, live-action games, and computer games. Prerequisite: CMPUT 250 or consent of the Program. [Faculty of Arts, Media and Technology Studies]Winter Term 2022