Reconnaissance Blind Chess is a chess variant designed for new research in artificial intelligence. RBC includes imperfect information, long-term strategy, explicit observations, and almost no common knowledge. These features appear in real-world scenarios, and challenge even state of the art algorithms. Each player of RBC controls traditional chess pieces, but cannot directly see the locations of her opponent's pieces. Rather, she learns partial information each turn by privately sensing a 3x3 area of the board. RBC's foundation in traditional chess makes it familiar and entertaining to human players, too!
There is no cost to enter this tournament. Winners will receive a small monetary prize and authors of the best AIs will be invited talk about their bots at NeurIPS, the world's largest AI conference.
Reconnaissance Blind Chess is now also a part of the new Hidden Information Games Competition (HIGC - http://higcompetition.info/
) being organized by DeepMind and the Czech Technical University in Prague.
Organized by:Johns Hopkins University Applied Physics Laboratory
Ashley J. Llorens (Microsoft Research)
Todd W. Neller (Gettysburg College)
Raman Arora (Johns Hopkins University)
Bo Li (University of Illinois)
Mykel J. Kochenderfer (Stanford University)