By the time of submission, their bot was the second in the global leaderboard. The paper has recently been published at the ICAART Conference (DOI: 10.5220/0012354200003636). The project was supervised by Zhisheng Huang and Shuai Wang. Frank van Harmelen was the second reader and provided valuable suggestions. Read more here: https://rbc.jhuapl.edu/.
Reconnaissance Blind Chess (RBC) is a chess variant designed for new research in artificial intelligence (AI). 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 chosen 3x3 area of the board.
RBC's foundation in traditional chess makes it familiar and entertaining to human players, too! You can invite a friend, challenge a bot, or compete for leaderboard ranking on this site, or through our mobile apps for Android and Apple.
Join the study and fun now by reading the rules, building a bot, or playing a game.