Mr. Seth Erway Profile

Seth Erway

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Proceedings Article | 7 June 2024 Presentation + Paper

A methodology for representing and assessing artificial intelligence decision aids within modeling and simulation

Joshua Wong, Emily Nack, Zachary Steelman, Seth Erway, Nathaniel Bastian

Proceedings Volume 13051, 130510K (2024) https://doi.org/10.1117/12.3013180

KEYWORDS: Artificial intelligence, Data modeling, Computer simulations, Monte Carlo methods, Modeling, Systems modeling, Machine learning

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Artificial intelligence (AI) is quickly gaining relevance as a transformative technology. Its ability to rapidly fuse and synthesize data, accelerate processes, automate tasks, and augment decision-making has the potential to revolutionize multi-domain warfighting through data-centric operations and algorithmic warfare. As the military relies more on AI-enabled Decision Aids to increase the efficiency and effectiveness of decision-making, it highlights the need to effectively assess them before deployment. Modeling and simulation (M&S) environments are essential for assessing these rapidly evolving AI-enabled systems. Accepted analytical frameworks are needed to guide ways to represent and model AI sufficiently within M&S environments for accurate assessment. In this paper, we identify common characteristics within the main categories of AI and investigate how those characteristics can be best represented across the main categories of M&S. We provide two use cases to highlight an assessment of AI-enabled Decision Aids for cybersecurity and aeromedical evacuation problems. Our example use cases demonstrate how to leverage a framework for analytic assessment of AI within M&S environments.

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