Darse Billings, Lourdes Peña, Jonathan Schaeffer, and Duane Szafron, University of Alberta
Until recently, artificial intelligence researchers who use games as their experimental testbed have concentrated on games of perfect information. 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 recent progress in developing a high-performance poker-playing program. The advances come in two forms. First, we introduce a new betting strategy that returns a probabilistic betting decision, a probability triple, that gives the likelihood of a fold, call or raise occurring in a given situation. This routine unifies all the expert knowledge used in the program, does a better job of representing the type of decision making needed to play strong poker, and improves the way information is propagated throughout the program. Second, real-time simulations are used to compute the expected values of betting decisions. The program generates an instance of the missing data, subject to any constraints that have been learned, and then simulates the rest of the game 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. Experimental results show that these enhancements each represent major advances in the strength of computer poker programs.