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“It’s Like Playing Chess Blind”: Competition Tests AI Decision-Making Under Uncertainty

Article Keywords
Decision Making

Last fall, more than 60 teams from around the world signed up for a competition developed by the Johns Hopkins Applied Physics Laboratory (APL), in Laurel, Maryland, to advance machine decision-making under uncertainty. Registrants hailed from top universities such as Carnegie Mellon and commercial leaders in artificial intelligence like Google and Microsoft.


In December, a group of APL staff members traveled to Vancouver to share the outcomes of the competition at the 2019 Conference on Neural Information Processing Systems (NeurIPS), considered the top AI conference in the world, and the Laboratory recently launched the 2020 Reconnaissance Blind Chess (RBC) league for the competition to support ongoing research, this time all online.


RBC organizers now have a dynamic online leader-board with cash prizes ($1,000 for first place and $500 for second place) offered to the top-ranked bots at the end of August 2020.