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).

翻译：定约桥牌是一种具有不完整信息特征的博弈，这为人工智能方法带来了激动人心的挑战。本文提出BridgeHand2Vec方法，利用神经网络将桥牌选手的手牌（由13张牌组成）嵌入到向量空间中。由此产生的表示反映了手牌在游戏中的强度，并能够确定不同手牌之间的可解释距离。该表示通过训练神经网络来估计一对搭档玩家能够赢得的墩数而获得。在本文后续部分，我们分析了所得向量空间的性质，并提供了其在强化学习和开叫分类中的应用实例。尽管这并非我们的主要目标，但用于向量化的神经网络在DDBP2问题（估计给定两手牌的墩数）上达到了最先进的水平。