Artificially intelligent (AI) warfare improves decision speed, simplifies dynamic battle spaces, and enables previously impossible missions. As an offset, AI will increasingly determine who wins, and loses, future conflicts. Yet, even as the U.S. advances AI in support of its own warfighters and to address adversarial overmatch, the machine learning techniques and structures that enable AI remain underdeveloped by the Department of Defense (DoD). Specifically, there is no standardized decision framework around the highly inventive, operationally appealing technology called for by government. In response, this study presents an architecture for measuring the effectiveness (MOE) of DoD autonomous battlefield systems. The architecture is founded on three theorems and described using a qualitative diagram. Each theorem proves a specific relationship within the MOE doctrine while the diagram assesses the existence of an end state that enables artificially intelligent warfare through minimally viable, unsupervised, deep learning-enabled, autonomous, and battlefield ready DoD systems. In doing so, the research addresses, for the first time, how artificially intelligent technology on the battlefield is assessed. As a novel assessment framework, this study amalgamates DoD doctrine with academic literature, accelerates DoD AI initiatives, fills a void in the current military paradigm, and presents a path away from conceptual focus vectors and toward tangible courses of action.
|Commitee:||Chantre, Mary Margaret, Leonard, Robert|
|School:||Capitol Technology University|
|School Location:||United States -- Maryland|
|Source:||DAI-A 81/2(E), Dissertation Abstracts International|
|Subjects:||Artificial intelligence, Systems science, Military studies|
|Keywords:||Artificial intelligence, Autonomous system, Department of defense, Machine learning, Measure of effectiveness, Warfare|
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