All are invited to participate in a computer science competition that will be part of the 2019 Conference on Neural Information Processing Systems (NeurIPS, https://nips.cc/), Reconnaissance Blind Chess.

Many of the favorite studied games in artificial intelligence (AI) such as checkers, chess, and Go lack something that is common and critical in real-life decision making, uncertainty.

This is a competition with a simple but powerful twist on what may be considered the most classic game in AI history, chess. Reconnaissance Blind Chess (RBC) is like chess except a player cannot see where her opponent's pieces are a priori. Rather, she
learns partial information about them with the ability to sense a 3x3 square of the board each turn and from the results of moves.

In comparison to poker, which seems to be the most popularly studied game of imperfect information, RBC includes a critical component of long-term planning. Compared to phantom games like Kriegspiel, in RBC players have much more ability to manage their
uncertainty, which we believe makes the game more interesting from an AI perspective and more realistic for most scenarios; players are not completely blind, but rather, metaphorically, they simply cannot look everywhere at once.

For more information on the NeurIPS competition or the game itself, or to play the game to get a feel for it, visit our website below.

All are welcome to create the best RBC bot they can, and see how well it can play against other bots in the tournament starting on October 21, 2019!

http://rbc.jhuapl.edu



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reddit discussion: https://www.reddit.com/r/MachineLearning/comments/bocf9v/r_reconnaissance_blind_chess_neurips_2019/?utm_source=share&utm_medium=web2x


twitter:
https://twitter.com/ryan_w_gardner/status/1128101573727531010
)

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