A work point count system coupled with back-propagation for solving double dummy bridge problem

The game ‘contract bridge’ is one of the most widely known card games comprising many fascinating aspects, such as bidding, playing and winning the trick including estimation of hand strength, the additional input data based on the human knowledge of the game to improve the quality of results. The game classified under a game of imperfect information is to be equally well-defined, since the decision made on any stage of the game is purely based on the decision that was made on the immediate preceding stage. The incompleteness of information, the real spirit of the card game in proceeding further deals of the game are taking into many forms especially during the distribution of cards for the next deal. One among the architectures of the Artificial Neural Network is considered by training on sample deals and used to estimate the number of tricks taken by one pair of bridge players is the key idea behind the Double Dummy Bridge Problem, implemented in this paper. The Cascade Correlation Neural Network architecture with supervised learning implemented to train data and hence to test it is coupled along with Work point count system.