Bridge is an imperfect information game with elements of competition against opponents as well as cooperation with a partner. Despite the application of many tenets of artificial intelligence, humans have yet to be consistently bested by the computer. This thesis explores AI shortcomings in both the play and bidding phases of the game.In the play, we explore weaknesses in the cutting edge Monte Carlo techniques and explore both inference and learning based solutions.In the bidding, we go beyond existing rule based systems and investigate deep reinforcement learning as a method to learn how to bid.
|School:||New York University|
|School Location:||United States -- New York|
|Source:||DAI-B 82/9(E), Dissertation Abstracts International|
|Subjects:||Computer science, Artificial intelligence|
|Keywords:||Computer bridge, Computer games|
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