  Artificial intelligence has seen several breakthroughs in recent years, with
games often serving as milestones. A common feature of these games is that
players have perfect information. Poker is the quintessential game of imperfect
information, and a longstanding challenge problem in artificial intelligence.
We introduce DeepStack, an algorithm for imperfect information settings. It
combines recursive reasoning to handle information asymmetry, decomposition to
focus computation on the relevant decision, and a form of intuition that is
automatically learned from self-play using deep learning. In a study involving
44,000 hands of poker, DeepStack defeated with statistical significance
professional poker players in heads-up no-limit Texas hold'em. The approach is
theoretically sound and is shown to produce more difficult to exploit
strategies than prior approaches.
