Operationalizing artificial intelligence for multi-domain operations: a first look

David K. Spencer; Stephen Duncan; Adam Taliaferro

doi:10.1117/12.2524227

10 May 2019 Operationalizing artificial intelligence for multi-domain operations: a first look

David K. Spencer, Stephen Duncan, Adam Taliaferro

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 1100602 (2019) https://doi.org/10.1117/12.2524227
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Artificial Intelligence / Machine Learning (AI/ML) is a foundational requirement for Multi-Domain Operations (MDO). To solve some of MDO’s most critical problems, for example, penetrating and dis-integrating an adversary’s antiaccess/area denial (A2/AD) systems, the future force requires the ability to converge capabilities from across multiple domains at speeds and scales beyond human cognitive abilities. This requires robust, interoperable AI/ML that operates across multiple layers: from optimizing technologies and platforms, to fusing data from multiple sources, to transferring knowledge across joint functions to accomplish critical MDO tactical tasks. This paper provides an overview of ongoing work from the Unified Quest Future Study Plan and other events with the Army’s Futures and Concepts Center to operationalize AI/ML to address MDO problems with this layered approach. It includes insights and required AI/ML capabilities determined with subject matter experts from various organizations at these learning events over the past two years, as well as vignettes that illustrate how AI/ML can be operationalized to enable successful Multi-Domain Operations against a near peer adversary.

Citation Download Citation

David K. Spencer, Stephen Duncan, and Adam Taliaferro "Operationalizing artificial intelligence for multi-domain operations: a first look", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100602 (10 May 2019); https://doi.org/10.1117/12.2524227

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