Christian Kündig, 2010
Master Thesis, University of Zürich
Poker offers an interesting domain to investigate some fundamental problems in artificial intelligence. The properties of stochasticity and imperfect information pose new challenging questions, not present in other typical game research subjects like chess; traditional methods for computer game-playing as alpha-beta search are incapable of handling these challenges.
This thesis presents the necessary algorithms to tackle these problems with the use of modified game tree search and opponent modeling. A proof-of-concept implementation for the game of No-Limit Texas Hold’em is provided (and benchmarked), based on the Miximax algorithm and an opponent model implemented as a Hoeffding tree.