The integration of modeling and simulation (M&S) into military training and system development has already provided warfighters with unprecedented opportunities for realistic scenario training. Now, a new technological wave is crashing onto the battlefield: artificial intelligence (AI). AI is not only enhancing virtual battlespace training but also refining real-world systems like drones.
AI and M&S: A Powerful Partnership
This shift has fostered collaborations between companies like CAE, a training system developer, and General Atomics, an aerospace platform provider. These partnerships leverage AI to enhance M&S applications. AI and machine learning models analyze vast amounts of simulation data to monitor events, identify trends, and extract valuable lessons.
Enhancing Training and Design
CAE, which provides training to the US Air Force, Army, and Navy, can now deliver more impactful training experiences. General Atomics Aeronautical Systems, known for its Reaper drones, gains valuable insights to improve its offerings to customers.
AI-Powered Training: A Force Multiplier
AI automates the creation of computer-generated forces in training scenarios, a practice that has existed for decades but is becoming more sophisticated. Instead of relying on real personnel to fill out virtual squads or serve as opposing forces, AI can simulate realistic and challenging opponents.
Brian Stensrud, technical fellow for artificial intelligence at CAE USA Defense & Security, explains, „The promise of AI is that I can use that to build models of behavior to serve as opponent forces and teammates at varying levels of complexity.” This reduces the logistical footprint, saves time and money, and enables more training hours for servicemembers.
Realistic Simulations for System Refinement
For platform developers like General Atomics, AI-powered simulations allow for testing new capabilities in realistic environments without the expense of actual flight testing. This enables rapid iteration and refinement of systems.
The Importance of Responsible AI Implementation
While the potential of AI is significant, it’s crucial to apply it thoughtfully. Anastacia MacAllister, technical director for autonomy and artificial intelligence at General Atomics, emphasizes, „We just need to make sure we’re using the right tool for the right job and ensuring that we understand the technology we’re using and the questions we’re asking it to answer.”
Unlocking Value in Training Data: Embedded Proficiency Analysis
AI is also revolutionizing proficiency analysis. CAE is developing an „omnipresent” AI observer that catalogs actions during training events, assesses performance, and provides feedback to instructors. This creates „good, actionable intelligence” to improve both student and instructor proficiency, according to Stensrud.
Data Quality and Context: Key to Effective AI
The effectiveness of AI in training depends on the quantity and quality of data. CAE uses machine learning algorithms to identify persistent proficiency issues, revealing flaws in curriculum or instruction. However, understanding the training mission and data nuances is crucial.
For example, telemetry data must be analyzed in the context of the mission objective. As MacAllister states, „Companies should instead treat data not necessarily as exhaust from our process, but should understand it as a resource that is going to drive the engine.”
Elevating Warfighter Skills and Capabilities
By combining good data with appropriate AI/ML algorithms, warfighters can develop necessary tactics and procedures before real-world operations. This data also improves the characteristics of aircraft. Through these opportunities, CAE and General Atomics are enhancing both warfighter skills and real-world capabilities to address modern threats.
