Artificial (military) intelligence: enabling decision dominance through machine learning

Kyle P. Hanratty

doi:10.1117/12.2663413

12 June 2023 Artificial (military) intelligence: enabling decision dominance through machine learning

Kyle P. Hanratty

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125380J (2023) https://doi.org/10.1117/12.2663413
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Decision dominance—a Commander’s ability to see, think, understand, and act first—becomes exponentially more important in multi-domain operations (MDO) where the lethality and pace of warfare increase. At the heart of decision dominance is intelligence analysis. Intelligence analysis provides an understanding of the threat and environment that forms the foundation for Commanders’ decisions throughout planning and execution. Artificial intelligence and machine learning (AI/ML) offer opportunities to automate portions of the intelligence process. Accordingly, this paper explores mechanisms to employ machine learning (ML) techniques to rapidly synthesize heterogeneous text data into knowledge that can be graphically depicted in a situation template (SITEMP). Specifically, the paper examines the feasibility of two approaches. The first leverages hierarchical, agglomerative clustering with subsequent classification. This approach is analogous to how an analyst unfamiliar with the environment would operate—seeking patterns in the data to develop a fused understanding. The second approach applies a few-shot learning methodology that is akin to an analyst recognizing reporting based on prior experience. While AI/ML is not a panacea, it does promise significant gains to the competitor that can leverage it first and most fully. Ultimately, this paper seeks to inspire novel applications of AI/ML technologies to combat the challenges expected in MDO.

Citation Download Citation

Kyle P. Hanratty "Artificial (military) intelligence: enabling decision dominance through machine learning", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380J (12 June 2023); https://doi.org/10.1117/12.2663413

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KEYWORDS

Machine learning

Army

Artillery

Visualization

Warfare

Analytics

Data modeling

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