Neural networks for contract bridge bidding
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The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.
[1] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[2] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[3] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[4] R. Palmer,et al. Introduction to the theory of neural computation , 1994, The advanced book program.