A common feature of games with these successes is that they involve information symmetry among the players, where all players have identical information. This property of perfect information, though, is far more common in games than in real-world problems.
Poker is the quintessential game of imperfect information, and it has been a longstanding challenge problem in artificial intelligence. In this paper researchers introduce DeepStack, a new algorithm for imperfect information settings such as poker. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition about arbitrary poker situations that is automatically learned from selfplay games using deep learning. In a study involving dozens of participants and 44,000 hands of poker, DeepStack becomes the first computer program to beat professional poker players in heads-up no-limit Texas hold'em. Furthermore, they show this approach dramatically reduces worst-case exploitability compared to the abstraction paradigm that has been favored for over a decade
DeepStack was evaluated against 33 professional poker players from the International Federation of Poker. Each participant was asked to play a 3,000-game match over a month.