RECONNAISSANCE BLIND CHESS

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A New Challenge for Decision-Making Under Uncertainty


Congratulations to everyone who participated in our NeurIPS 2019 tournament.

We expect to have a dynamic leaderboard and updated match mechanism in early 2020. All are welcome to build a bot now and see what they can learn and how their algorithms will rank.

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

We are studying and having fun 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 through chosen sensing actions and 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.

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