Poker provides an excellent testbed for studying decision-making under
conditions of uncertainty.
There are many benefits to be gained from designing and
experimenting with poker programs.
It is a game of imperfect knowledge, where multiple competing agents
must understand estimation, prediction, risk management,
deception, counter-deception, and agent modeling.
New evaluation techniques for estimating the strength and potential
of a poker hand are presented. This thesis describes the implementation of
a program that successfully handles all aspects of the game, and
uses adaptive opponent modeling to improve performance.