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Reconnaissance Blind Chess + Q&A
Ryan Gardner · Gino Perrotta · Corey Lowman · Casey Richardson · Andrew Newman · Jared Markowitz · Nathan Drenkow · Bart Paulhamus · Ashley J Llorens · Todd Neller · Raman Arora · Bo Li · Mykel J Kochenderfer

Wed Dec 08 02:25 AM -- 02:45 AM (PST) @
in Competition Track Day 2: Overviews + Breakout Sessions »
Event URL: https://rbc.jhuapl.edu »

Reconnaissance Blind Chess is like chess except a player cannot see her opponent's pieces in general. Rather, each player chooses a 3x3 square of the board to privately observe each turn. Algorithms used to create agents for previous games like chess, Go, and poker break down in Reconnaissance Blind Chess for several reasons including the imperfect information, absence of obvious abstractions, and lack of common knowledge. In addition to this NeurIPS competition, the game is recently part of the new Hidden Information Games Competition (HIGC) that is organized with the AAAI Reinforcement Learning in Games workshop (2022). Build the best bot for this challenge in making strong decisions in multi-agent scenarios in the face of uncertainty.

Author Information

Ryan Gardner (Johns Hopkins University Applied Physics Laboratory)
Gino Perrotta (The George Washington University)
Corey Lowman (Johns Hopkins University Applied Physics Laboratory)
Casey Richardson (Johns Hopkins University Applied Physics Lab)
Andrew Newman (Johns Hopkins University Applied Physics Laboratory)
Jared Markowitz (Johns Hopkins Applied Physics Laboratory)
Nathan Drenkow (The Johns Hopkins University Applied Physics Laboratory)
Bart Paulhamus (Johns Hopkins Applied Physics Laboratory)
Ashley J Llorens (Microsoft Research)
Todd Neller (Gettysburg College)
Raman Arora (Johns Hopkins University)
Bo Li (UIUC)
Mykel J Kochenderfer (Stanford University)

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