RECONNAISSANCE BLIND CHESS
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Official Online Competitions
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! On conclusion of the competition, penumbra attained a BayesELO rating of 1705 and La-Q Bot a rating of 1669. We hope to hear more information about their approaches soon. 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!
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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Current Bot Leaderboard
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* Bots must play at least 100 games before they are eligible for prizes.