NEURAL NETWORKS FOR CONTRACT BRIDGE BIDDING

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 a rtificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new han d. The game of bridge, like many other games used in artificial intell igence, can easily be represented ina 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 th e precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-lev el opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.