The First International Competition in Machine Reconnaissance Blind Chess
; Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:121-130, 2020.
Reconnaissance blind chess (RBC) is a chess variant in which a player cannot see her opponent’s pieces but can learn about them through private, explicit sensing actions. The game presents numerous research challenges, and was the focus of a competition held in conjunction with of the 2019 Conference on Neural Information Processing Systems (NeurIPS). The 22 bots that played in the tournament leveraged a diverse set of algorithms, including variations of multi-state tracking, piece-wise probability estimation, Gibbs sampling, bandit algorithms, tree search, counterfactual regret minimization (CFR), deep learning, and others. None of the algorithms of which we are aware converges to an optimal strategy. Top algorithms generally incorporated sensing strategies that successfully minimized uncertainty (as measured in the number of possible opponent states). The top two approaches reduced this raw uncertainty metric less than some others. Successful strategies sometimes defied conventional wisdom in chess, as evidenced by deviations between win rate and aggregate move strength as assessed by the leading available chess engine.