model

Innovative model improves Army human-agent teaming

ADELPHI, Md. — Army researchers developed a novel computational model for gathering cognitive data that may be a game changer in the fields of neuroscience and econometrics, and has broad relevance to networked and multi-agent systems.

At the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory and the University of Maryland, College Park, researchers developed what is known as the Autoregressive Linear Mixture, or ALM, a novel model for analyzing time-series data, or how things change over time.

For the Army, this comes in to play to assess the cognitive states of Soldiers to allow an intelligent adaptive system to balance the workload of the crew when completing challenging critical missions.

Employing recent advancements in optimization for nonconvex problems, the researchers adapted a proximal gradient algorithm and validated the proposed model and algorithm on an open-source electroencephalography, or EEG, dataset.

This work, recently featured in IEEE Xplore, innovates

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