Who should bid higher, NS or WE, in a given Bridge dealƒ

Mańdziuk, Jacek; Suchan, Jakub

doi:10.1109/ijcnn.2019.8851900

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…In the paper [17], the authors formulate two Double Dummy Bridge Problem (DDBP) type problems: DDBP2 -where only two hands are known, and DDBP4 -where all four hands are known. Various works have analyzed this problem, including [9,13,14,17]. In this study, we train a neural network to solve the DDBP2 problem to obtain a vector representation, BridgeHand2V ec, of the hand.…”

Section: Related Workmentioning

confidence: 99%

BridgeHand2Vec Bridge Hand Representation

Sztyber-Betley,

Kołodziej,

Betley

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Contract bridge is a game characterized by incomplete information, posing an exciting challenge for artificial intelligence methods. This paper proposes the BridgeHand2Vec approach, which leverages a neural network to embed a bridge player’s hand (consisting of 13 cards) into a vector space. The resulting representation reflects the strength of the hand in the game and enables interpretable distances to be determined between different hands. This representation is derived by training a neural network to estimate the number of tricks that a pair of players can take. In the remainder of this paper, we analyze the properties of the resulting vector space and provide examples of its application in reinforcement learning, and opening bid classification. Although this was not our main goal, the neural network used for the vectorization achieves SOTA results on the DDBP2 problem (estimating the number of tricks for two given hands).

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