Neural networks for contract bridge bidding

• Published in: Sadhana (Bangalore) Vol. 21; no. 3; pp. 395 - 413
• Main Authors: Yegnanarayana, B, Khemani, Deepak, Sarkar, Manish
• Format: Journal Article
• Language: English
• Published: 01-06-1996
• Subjects: Artificial intelligence; Backpropagation; Feedforward neural networks; Game theory; Hierarchical systems; Knowledge engineering; Learning systems; Pattern recognition
• ISSN: 0256-2499; 0973-7677
• DOI: 10.1007/BF02745531

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.