RAND researchers explored the capabilities and limitations of future weapon methods incorporating synthetic intelligence and machine studying (AI/ML) by two wargame experiments. The researchers modified and augmented the principles and engagement statistics utilized in a business tabletop wargame to allow (1) remotely operated and absolutely autonomous fight automobiles and (2) automobiles with AI/ML–enabled situational consciousness to point out how the 2 sorts of automobiles would carry out in company-level engagements between Blue (U.S.) and Crimson (Russian) forces. These guidelines sought to realistically seize the capabilities and limitations of these methods, together with their vulnerability to chose enemy countermeasures, resembling jamming. Future work may enhance the realism of each the gameplay and illustration of AI/ML–enabled methods.
On this experiment, members performed two video games: a baseline recreation and an AI/ML recreation. All through play within the two recreation situations, gamers on each side mentioned the capabilities and limitations of the remotely operated and absolutely autonomous methods and their implications for partaking in fight utilizing such methods. These discussions led to modifications in how the methods had been employed by the gamers and observations about which limitations needs to be mitigated earlier than commanders had been more likely to settle for a system and which capabilities wanted to be absolutely understood by commanders in order that methods may very well be employed appropriately.
This analysis demonstrated how such video games, by bringing collectively operators and engineers, may very well be utilized by the necessities and acquisition communities to develop realizable necessities and engineering specs for AI/ML methods.
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