Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/4394
Title: | Artificial Intelligence Systems: Unique Challenges for Defense Applications |
Authors: | Bonnie Johnson |
Keywords: | Artificial intelligence machine learning complexity tactical decision aids systems engineering trust human–machine teaming |
Issue Date: | 10-May-2021 |
Publisher: | Acquisition Research Program |
Citation: | Published--Unlimited Distribution |
Series/Report no.: | Acquisition Management;SYM-AM-21-087 |
Abstract: | Today’s warfighters are bombarded with information and faced with challenging decision spaces as technology exponentially expands and threat environments become more complex. Artificial intelligence (AI) and machine learning (ML) are advancements that can lessen the burden on the warfighter. AI systems offer far-reaching benefits—improving situational awareness and detection and understanding of threats and adversary capabilities and intents; identifying and evaluating possible tactical courses of action; and offering methods to predict outcomes and effects of course of action decisions. AI systems are the key to understanding and addressing highly complex tactical situations. AI systems offer advantages to the warfighter, but only if these systems are engineered and implemented correctly and in a manner that lessens the warfighter’s cognitive load. Implementing AI systems for defense applications presents unique challenges. This paper identifies four unique challenges and describes how they affect the tactical warfighter, the engineering design community, and national defense. This paper offers solution ideas for addressing these unique challenges through defense acquisition and systems engineering initiatives. |
Description: | Acquisition Management / Defense Acquisition Community Contributor |
URI: | https://dair.nps.edu/handle/123456789/4394 |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Description | Size | Format | |
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SYM-AM-21-087.pdf | 1.29 MB | Adobe PDF | View/Open |
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