The programs were performing well and it is also matter of time before the computer beats most human bridge players. As such, the researchers mainly focused on the techniques and computer programs which were used in bridge bidding and playing phases. | ISSN:2249-5789 M Dharmalingam et al, International Journal of Computer Science & Communication Networks,Vol 2(1), 140-146 Techniques and Programs Used in Bidding and Playing phases of Imperfect Information Game – an Overview M DHARMALINGAM Research Scholar, Department of Computer Science D J Academy for Managerial Excellence Bharathiar University, Coimbatore Tamil Nadu – 641032, India emdharma@ Dr E CHANDRA Director, Department of Computer Science and Applications Dr SNS Rajalakshmi Arts and Science College Bharathiar University, Coimbatore Tamil Nadu – 641049, India Abstract—Bridge is an international imperfect information game played with similar rules all over the world and it is played by millions of players. It is an intelligent game; it increases creativity and knowledge of human mind. Many of the researchers analyses the Bridge bidding and playing phases, and they developed many programs for getting better results. The programs were performing well and it is also matter of time before the computer beats most human bridge players. As such, the researchers mainly focused on the techniques and computer programs which were used in bridge bidding and playing phases. Keywords — Artificial Neural Network, Game of Bridge, DDBP, Bridge Bidding, Playing Bridge, Monte – Carlo Approach. I. INTRODUCTION Artificial neural networks trained only on sample deals without presentation of any human knowledge or even rules of the game and used to the estimate the number of tricks to be taken by one pair of bridge players in the so-called Double Dummy Bridge Problem [2]. This paper briefly reviewed about the computer programs and languages which were used to play the bridge game. II. ARTIFICIAL NEURAL NETWORKS IN BRIDGE GAME Artificial Neural Network for making an opening bid in the game of contract bridge. The neural network captures the implicit mapping in bidding a bridge hand adopting a standard convention which acts as a guide or weak constraint on the mapping .
