AI Bibliography
AI Bibliography |
|
 |
 |
 |
|
Spencer, D. K., Duncan, S., & Taliaferro, A. 2019, Operationalizing artificial intelligence for multi-domain operations: A first look. Paper presented at Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. |
| Resource type: Proceedings Article BibTeX citation key: Spencer2019 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, General, Geopolitical, Military Science Subcategories: Army, Autonomous systems, China, Cognitive Electronic Warfare, Command and control, Cross-domain deterrence, Cyber, Doctrine, Edge AI, Human decisionmaking, JADC2, Military research, Mosaic warfare, Networked forces, Psychology of human-AI interaction, Russia, Strategy, United States Creators: Duncan, Spencer, Taliaferro Publisher: International Society for Optics and Photonics Collection: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications |
| Attachments |
| 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.
|