Artificial neural networks for solving double dummy bridge problems

Abstract

This paper describes the results of applying artificial neural networks to the double dummy bridge problem. Several feedforward neural networks were trained using resilient backpropagation algorithm to estimate the number of tricks to take by players N S in fully revealed contract bridge deals. Training deals were the only data presented to the networks. The best networks were able to perfectly point the number of tricks in more than one third of deals and gained about 80% accuracy when one trick error was permitted. Only in less than 5% of deals the error exceeded 2 tricks.