With the fast progress in perfect information game problems such as AI chess and AI Go, researchers have turned to imperfect information game problems, including Texas Hold’em and Bridge. Bridge is one of the most challenging card games that have significant research value. Bridge playing is divided into two phases: bidding and playing. This paper focuses on bridge bidding and proposes a bridge bidding service framework using deep neural networks, and supports bidding visualization for the first time. The framework consists of two parts: the bidding model (BM) with a multilayer neural network, and a visualization system. The framework predicts not only reasonable bids from the existing bidding system of humans, but also provides intuitive explanations for decisions to enable human–computer information interaction. Experimental results show that this bidding AI outperforms majority of existing systems.

Artificial intelligence (AI) has made many breakthroughs in the perfect information game. Nevertheless, Bridge, a multiplayer imperfect information game, is still quite challenging. Bridge consists of two parts: bidding and playing. Bidding accounts for about 75% of the game and playing for about 25%. Expert-level teams are generally indistinguishable at the playing level, so bidding is the more decisive factor in winning or losing. The two teams can communicate using different systems during the bidding phase. However, existing bridge bidding models focus on at most one bidding system, which does not conform to the real game rules. This paper proposes a deep reinforcement learning model that supports multiple bidding systems, which can compete with players using different bidding systems and exchange hand information normally. The model mainly comprises two deep neural networks: a bid selection network and a state evaluation network. The bid selection network can predict the probabilities of all bids, and the state evaluation network can directly evaluate the optional bids and make decisions based on the evaluation results. Experiments show that the bidding model is not limited by a single bidding system and has superior bidding performance.