Reconnaissance Blind Chess

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A Challenge for Machine Decision Making under Uncertainty

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. Other popular games like poker lack a significant component of long term strategy or planning.

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.

Compared to phantom games like Kriegspiel and games like dark chess, in RBC players have much more ability to manage their uncertainty with explicit sensing actions, which we believe makes the game more interesting from an AI perspective and more realistic for many scenarios; players are not completely blind, but rather, metaphorically, they simply cannot look everywhere at once.

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2020 Leaderboard Challenge

Congratulations to Gregory Clark, author of penumbra, and Kyle Blowitski and Timothy (T.J.) Highley, authors of La-Q Bot, who won $1000 and $500 respectively in our most recent scheduled online competition! Hear more in an interview with JHU/APL and Clark. Thank you to all who participated.

Although the cash prize has been awarded, the competition to create one of the top bots on the leaderboard is ongoing!


NeurIPS 2019 Tournament

Congratulations to the participants of our NeurIPS 2019 tournament!


Current Bot Leaderboard

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