Supervised Elman Neural Network Architecture for Solving Double Dummy Bridge Problem in Contract Bridge

M Dharmalingam, R Amalraj

Abstract: Contract Bridge is an intelligent game; which enhances the creativity with multiple skills and quest to acquire the intricacies of the game; because no player knows exactly what moves other players are capable of during their turn. The Bridge being a game of imperfect information is to be equally well defined; since the outcome at any intermediate stage is purely based on the decision made on the immediate preceding stage. One among the architectures of Artificial Neural Networks (ANN) is applied by training on sample deals and used to estimate the number of tricks to be taken by one pair of bridge players is the key idea behind Double Dummy Bridge Problem (DDBP) implemented with the neural network paradigm. This study mainly focuses on Elman Neural Networks (ENN) which is used to solve the Bridge problem by using Resilient Back-Propagation (RBP) Algorithm and Work Point Count System.

Keywords: Artificial Neural Network, Elman Neural Network, Resilient Back-Propagation Algorithm, Activation functions, Contract Bridge, Double Dummy Bridge Problem, Work Point Count System

How to Cite?: M Dharmalingam, R Amalraj, "Supervised Elman Neural Network Architecture for Solving Double Dummy Bridge Problem in Contract Bridge", Volume 3 Issue 6, June 2014, International Journal of Science and Research (IJSR), Pages: 2745-2750, https://www.ijsr.net/getabstract.php?paperid=2014874, DOI: https://dx.doi.org/10.21275/2014874