M. Dharmalingam, Bharathiar University Arts and Science College, Erode, Tamilnadu, India.
International Journal of Advanced Information and Communication Technology
Received On : January 07, 2020
Revised On : February 10, 2020
Accepted On : March 12, 2020
Published On : April 05, 2020
Volume 07, Issue 04
Pages : 044-052
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 Cascade-Correlation Neural Network (CCNN) and Elman Neural Network (ENN) which is used to solve the Bridge problem by using Resilient Back-Propagation (R-prop) Algorithm and Work Point Count System.
Keywords
ANN; CCNN; ENN; R-prop; Sigmoid activation functions; Contract Bridge; DDBP; Bidding; Playing; WPCS.
Cite this article
M. Dharmalingam, “Artificial Neural Network Architectures for Solving the Contract Bridge,” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 44–52, Feb. 2020.
Copyright
© 2020 M. Dharmalingam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.