Action clusters: an NLP-powered approach to detecting similarities of actions within battles

Rebecca Coulson; Manuel M. Vindiola; Song Park; Kevin Corder

doi:10.1117/12.2669527

12 June 2023 Action clusters: an NLP-powered approach to detecting similarities of actions within battles

Rebecca Coulson, Manuel M. Vindiola, Song Park, Kevin Corder

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125380C (2023) https://doi.org/10.1117/12.2669527
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Battles and simulated battles are typically analyzed using standard statistics, e.g. the number of units damaged, the duration of combat, or the adherence to battle plan. Higher-level descriptors, however, are needed to truly understand and describe a battle. For example, these standard statistics are incapable of detecting or describing collaboration. Our work implements an NLP-inspired algorithm which analyzes battle actions and detects higher-level similarity among battle actions. Additionally, our work suggests possible applications of this technique.

Citation Download Citation

Rebecca Coulson, Manuel M. Vindiola, Song Park, and Kevin Corder "Action clusters: an NLP-powered approach to detecting similarities of actions within battles", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380C (12 June 2023); https://doi.org/10.1117/12.2669527

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