| Autor: |
Tarraf, Danielle C, Gilmore, J Michael, Barnett, D Sean, Boston, Scott, Frelinger, David R, Gonzales, Daniel, Hou, Alexander C, Whitehead, Peter |
| Zdroj: |
JDMS: The Journal of Defense Modeling and Simulation; 20240101, Issue: Preprints |
| Abstrakt: |
In this report, researchers experimented with how postulated artificial intelligence/machine learning (AI/ML) capabilities could be incorporated into a wargame. We modified and augmented the rules and engagement statistics used in a commercial tabletop wargame to enable (1) remotely operated and fully autonomous combat vehicles and (2) vehicles with AI/ML-enabled situational awareness to show how the two types of vehicles would perform in company-level engagement between Blue (US) and Red (Russian) forces. The augmented rules and statistics we developed for this wargame were based in part on the US Army’s evolving plans for developing and fielding robotic and AI/ML-enabled weapon and other systems. However, we also portrayed combat vehicles with the capability to autonomously detect, identify, and engage targets without human intervention, which the Army does not presently envision. The rules we developed sought to realistically portray the capabilities and limitations of AI/ML-enabled systems, including their vulnerability to selected enemy countermeasures, such as jamming. Future work could improve the realism of both the gameplay and representation of AI/ML-enabled systems, thereby providing useful information to the acquisition and operational communities in the US Department of Defense. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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