The Department of Defense plans to spend $1.7 billion over the next five years to stand up a new Joint Artificial Intelligence Center with goals to develop strategic plans, adopt and transition artificial intelligence,
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The Government’s “Cloud First” policy (Kundra, 2011) set an accelerated course of government technology migration to cloud resources. Traditional on-premises visual systems seem ripe for cloud migration because the systems are expensive, dedicated, compute-intensive machines with large physical footprints and requirements for regular maintenance, upgrades, and environment control.
Operation Blended Warrior (OBW) 2016 marked the second year of a three-year effort to document lessons learned and understand barriers to implementing Live, Virtual, Constructive (LVC) distributed training. In the first year of the event, LVC focus areas included connectivity, interoperability, data standards, after-action review, and cyber security. Year two introduced additional focus areas: multi-level security, cross domain solutions, long-haul feeds, and performance measurement. This paper focuses on this latter area—defining and collecting performance measures.
Several trends within the simulation and training industry are emphasizing the need for measurable proof that training solutions meet or exceed the requirements for delivering effective training. Cognitive state is a key component of learning, meaning that classification of cognitive state and capacity can provide a measure of training effectiveness. However, accurate classification of trainee state is an extremely challenging task. The more traditional subjective assessment methods have several limitations, while objective assessment methods can be difficult to implement.
The limited field of view of static egocentric visual displays employed in unmanned aircraft controls introduces the
soda straw effect on operators, which significantly affects their ability to capture and maintain situational awareness
by not depicting peripheral visual data.
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