RECONNAISSANCE BLIND CHESS
Join our 2019 NeurIPS Competition!
A Challenge for Making Optimal Decisions Under Uncertainty
Everyone is invited to build a bot to compete in our NeurIPS 2019 competition online starting October 21, 2019. There is no cost to compete and competitors do not need to attend the NeurIPS conference. See the schedule for other important deadlines.
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 hosting a competition 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.