Systems and Industrial Engineering Seminar

Building an Artificial Poker Player - A Blend of Probability and Statistics, Decision Making, Game Theory, and Psychology
Mary Domm & Jeff Goldberg
The University of Arizona

Abstract

Games of strategy and games of chance have long been an avocation of mathematicians and engineers.  Efforts range from the study of probability by Pascal (motivated by the problem of developing winning gambling strategies) to the encoding of chess playing algorithms in hardware and software in IBM's "Deep Blue".  Generally these studies have been limited to deterministic and stochastic games with perfect information - for example chess and checkers, or dice and video poker.   The studies have been largely successful to the extent that computer chess and checker programs can compete with the best human players in the world and we can compute strategies that maximize the expected value of stochastic games.

In this talk we discuss the game of poker in the framework of a stochastic game with imperfect information.  The stochastic element comes from the random deal of the cards, while the imperfect information arises from the deception used by opponents.  Our eventual goal is to develop a computer poker player that can compete successfully with human players.  We use the game of "Texas Hold'em" as our study game due to its simple structure and popularity.   Now, Texas Holdem can be played in legal poker rooms across the country (except a few non-gambling states) and in virtual poker rooms on the Internet. 

In the talk we first motivate why poker is in some sense more difficult for a computer to play well than chess.  We review earlier efforts at developing computer players and show that their major shortcoming is in opponent modeling.  If one cannot develop strategies based on your opponents and their behaviors, then you will generally be unsuccessful at the game (you will be making many decisions that have negative expectation).  We discuss the components of an opponent model and initial efforts at categorizing players using data mining on a data set from IRC Poker games.   We conclude with some directions for future research.
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Note: Refreshments will be served.

DATE AND TIME:    2 PM, Thursday, November 29, 2001
PLACE:                       Engineering Building  (Bldg. #20), Room 30 1

Sponsored by the Department of Systems and Industrial Engineering.
For more information contact: Meg Askey , 621-2274