Poker is a challenging problem for artificial intelligence research: multiple opponents (up to 10), stochastic element (cards being dealt), imperfect information (don't know the opponent's cards), deception (bluffing), user modeling (identifying player patterns), and risk management (betting decisions). Unlike the classic AI game, chess, poker is more relevant to real-world situations including negotiations, military strategy, and e-commerce.
For over a decade, the University of Alberta Computer Poker Group has been working on building a high-performance poker program. This work has led us through multiple distinct phases of program design, each new idea "promising" to be the breakthrough to world-class play. Finally, we appear to be close. In a recent Man Versus Machine Match, University of Alberta programs narrowly lost to two world-class players. In this talk we will motivate the research, compare the different program designs, and discuss what it will take to raise the stakes in man-machine poker competitions.