AI/ML applications are critical to the success of future Multi-Domain Operations (MDO). Joint Forces and Coalition Partners require the ability to converge capabilities from across multiple echelons at speeds and scales beyond human cognition. At the tactical edge, future military operations will involve teams of highly-dispersed warfighters and agents (robotic and software) operating in distributed, dynamic, complex, cluttered environments. Military domains are frequently distinct from commercial applications because of: rapidly changing situations; limited access to real data to train AI; noisy, incomplete, uncertain, and erroneous data inputs during operations; and peer adversaries that employ deceptive techniques to defeat algorithms. Most current research in AI/ML is accomplished with extremely large collections of relatively clean, well-curated training/operational data with little background noise and no deception.

The military has unique technical challenges that the commercial sector will not address as it will increasingly: (i) engage in distributed operations in complex settings, (ii) operate with extreme resource constraints (communications, computational, and size-weight-power-cost-time), (iii) learn in complex data environments with limited and potentially compromised data samples; and (iv) rely on rapidly-adaptable teams of autonomous AI systems that interact and learn from human understanding of high-level mission goals. Most importantly, reliance by the warfighter on AI at the tactical edge will require AI that is reliable and safe, robust to multiple, varying adversarial attacks and adaptive to evolving environments and mission tasks.

The goals of this conference are (i) to promote understanding of near-term and far-term implications of AI/ML for MDO and (ii) to gain awareness of R&D activities in AI/ML that are applicable to MDO. Topics include but are not limited to the following:

For 2022, we plan to have joint sessions with other SPIE DCS conferences in the Next Generation Sensors & Applications track including: All accepted and presented full papers at Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV will contest for Best Conference Paper Award and Best Student Paper Award. Only Students will be considered for the Best Student Paper Award. Papers will be evaluated on originality, technical merit, and significance to MDO applications. Only manuscripts received by 9 Mar 2022 will be considered.

*Winners will be announced at the conclusion of the symposium.

*Program Committee Chair papers are excluded from all Best Paper Awards.;
In progress – view active session
Conference SI210

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV

This conference has an open call for papers:
Abstract Due: 6 October 2021
Author Notification: 6 December 2021
Manuscript Due: 9 March 2022
AI/ML applications are critical to the success of future Multi-Domain Operations (MDO). Joint Forces and Coalition Partners require the ability to converge capabilities from across multiple echelons at speeds and scales beyond human cognition. At the tactical edge, future military operations will involve teams of highly-dispersed warfighters and agents (robotic and software) operating in distributed, dynamic, complex, cluttered environments. Military domains are frequently distinct from commercial applications because of: rapidly changing situations; limited access to real data to train AI; noisy, incomplete, uncertain, and erroneous data inputs during operations; and peer adversaries that employ deceptive techniques to defeat algorithms. Most current research in AI/ML is accomplished with extremely large collections of relatively clean, well-curated training/operational data with little background noise and no deception.

The military has unique technical challenges that the commercial sector will not address as it will increasingly: (i) engage in distributed operations in complex settings, (ii) operate with extreme resource constraints (communications, computational, and size-weight-power-cost-time), (iii) learn in complex data environments with limited and potentially compromised data samples; and (iv) rely on rapidly-adaptable teams of autonomous AI systems that interact and learn from human understanding of high-level mission goals. Most importantly, reliance by the warfighter on AI at the tactical edge will require AI that is reliable and safe, robust to multiple, varying adversarial attacks and adaptive to evolving environments and mission tasks.

The goals of this conference are (i) to promote understanding of near-term and far-term implications of AI/ML for MDO and (ii) to gain awareness of R&D activities in AI/ML that are applicable to MDO. Topics include but are not limited to the following:

  • Learning and reasoning with small data samples, dirty data, high clutter, and deception
  • Autonomous maneuver in complex environments
  • Federated/distributed AI/ML
  • Human agent teaming
  • AI-enable context-aware decision making
  • Resource-constrained AI processing at the point-of-need
  • Adversarial machine learning
  • Interpretable and explainable AI
  • Novel AI/ML algorithms, frameworks and applications
  • Modeling & Simulation Platforms for AI
  • Safety, ethics and governance
  • Future trends in AI to including 5G and AI, EW and AI, broad AI, quantum AI, AI with additive manufacturing, AI with synthetic biology…
For 2022, we plan to have joint sessions with other SPIE DCS conferences in the Next Generation Sensors & Applications track including:
  • Virtual, Augmented & Mixed Reality (XR) Technology for Multi-Domain Operations III
  • Unmanned Systems Technology XXIV
  • Disruptive Technologies in Information Sciences VI
All accepted and presented full papers at Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV will contest for Best Conference Paper Award and Best Student Paper Award. Only Students will be considered for the Best Student Paper Award. Papers will be evaluated on originality, technical merit, and significance to MDO applications. Only manuscripts received by 9 Mar 2022 will be considered.

*Winners will be announced at the conclusion of the symposium.

*Program Committee Chair papers are excluded from all Best Paper Awards.
Conference Chair
CCDC Army Research Lab. (United States)
Conference Chair
CCDC Army Research Lab. (United States)
Conference Co-Chair
Myron E. Hohil
CCDC Armament Ctr. (United States)
Conference Co-Chair
Ravi Ravichandran
BAE Systems (United States)
Program Committee
Univ. of Illinois (United States)
Program Committee
Jan Paolo Acosta
U.S. Army Research Lab. (United States)
Program Committee
U.S. Military Academy (United States)
Program Committee
Air Force Office of Scientific Research (United States)
Program Committee
Gerome Bovet
Armasuisse (Switzerland)
Program Committee
Kevin Chan
CCDC Army Research Lab. (United States)
Program Committee
IBM United Kingdom Ltd. (United Kingdom)
Program Committee
Nandi O. Leslie
Raytheon Intelligence & Space (United States)
Program Committee
Univ. of Calgary (Canada)
Program Committee
Gavin Pearson
Defence Science and Technology Lab. (United Kingdom)
Program Committee
Cardiff Univ. (United Kingdom)
Program Committee
Katie Rainey
Naval Information Warfare Ctr. Pacific (United States)
Program Committee
Howard Univ. (United States)
Program Committee
Kelly K. D. Risko
U.S. Army Aviation and Missile Command (France)
Program Committee
Mitre (United States)
Program Committee
Lee Seversky
Air Force Research Lab. (United States)
Program Committee
CCDC Army Research Lab. (United States)
Program Committee
Xiaogang Wang
CCDC Army Research Lab. (United States)
Program Committee
Johns Hopkins Univ. Applied Physics Lab., LLC (United States)
Additional Information