Intelligent Systems Center

inventing the future of intelligent systems for our nation

Our Mission

Reconnaissance Blind Chess

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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Active Sensing Testbed

At the Johns Hopkins Applied Physics Laboratory’s Intelligent Systems Center, researchers have created the Active Sensing Testbed – a development environment for integrated pattern recognition and reasoning – to help enable progress in machine perception beyond singular narrowly-trained algorithms and to accelerate the translation of research advancements to real-world application.

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Our Facility

Our state-of-the-art spaces and lab capabilities support advanced research in artificial intelligence, robotics and neuroscience.

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