Using Selective-Sampling Simulations in Poker

Darse Billings, Denis Papp, Lourdes Peña, Jonathan Schaeffer, and Duane Szafron

Until recently, AI research that used games as an experimental testbed has concentrated on perfect information games. Many of these games have been amenable to so-called brute-force search techniques. In contrast, games of imperfect information, such as bridge and poker, contain hidden knowledge making similar search techniques impractical. This paper describes work being done on developing a world-class poker-playing program. Part of the program’s playing strength comes from real-time simulations. The program generates an instance of the missing data, subject to any constraints that have been learned, and then searches the game tree to determine a numerical result. By repeating this a sufficient number of times, a statistically meaningful sample can be obtained to be used in the program’s decision-making process. For constructing programs to play two-player deterministic perfect information games, there is a well-defined framework based on the alpha-beta search algorithm. For imperfect information games, no comparable framework exists. In this paper we propose selective sampling simulations as a general-purpose framework for building programs to achieve high performance in imperfect information games.

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