Applying first order logic decision tree induction to opponent modelling in No-limit Texas Hold'em Poker
Summary
In this thesis methods will be discussed to attempt to improve the decision making
for agents involved in playing Texas Hold'em poker. Poker is a card game which
features uncertainty, hidden information, deception and an environment in which
multiple agents compete with each other to win the game. Opponent modelling will
play a key role in improving the decision making of the agent(s).
The opponent models will help the agents to adapt more quickly to the playing
style of their opponents and help them to make a better prediction of their
next actions. In order for the agents to cope better with changes in playing style
or the environment, it is important that the models are dynamic and keep
evolving during the game.
Tilde, a first-order logic decision tree induction algorithm, will be used to
construct models from a dataset. In turn, Monte-Carlo tree search will yield
the action with the highest expected value after simulation. The opponent models
will guide the simulation.