The poker game we developed uses a simplified poker model, explained here, to add some intelligence to the computer moves. The computer decides what to do based on the UnBBayes API and the poker Bayesian network. The poker game developed is a Computer X User poker game where you can bet, 5 points, or quit.
The class diagram of the main classes used is shown here:
Poker is a Bayesian network that models a simplified version of the game of poker. The network can help predict who has the best hand - your or your opponent.
To download the poker game, please click here.
This example originates from the book An introduction to Bayesian Networks.
Consider a simplified poker game with the following rules:
The nodes OH0 , OH1 and OH2 represent the opponent's hand initially, after the first change (FC) and after the second change (SC) respectively. MH represents my hand before the bidding starts. Note that we have exact knowledge of the state of this node, so the initial distribution is not important.
Of course the probabilities of the various states of OH2 depend on the presumed strategy of the opponent.
In the network enclosed, the following strategy for the opponent is assumed:
Now you can enter the states of FC , SC and MH and get the probability of you having the best hand.
This network is available with the game.