Anaheim Convention Center
Anaheim, California, United States
26 - 30 April 2020
Anaheim, California, United States
26 - 30 April 2020
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
This conference has an open call for papers:
submit an abstract
(sign in required)
Submission guidelines for Authors and Presenters
submit an abstract
(sign in required)
Submission guidelines for Authors and Presenters
Important
Dates
Dates
show | hide
Abstract Due:
16 October 2019
Author Notification:
20 December 2019
Manuscript Due Date:
1 April 2020
16 October 2019
Author Notification:
20 December 2019
Manuscript Due Date:
1 April 2020
Conference
Committee
Committee
show | hide
Conference Chair
Conference Co-Chairs
Program Committee
- Tien Pham, CCDC Army Research Lab. (United States)
Conference Co-Chairs
- Latasha Solomon, CCDC Army Research Lab. (United States)
- Katie Rainey, Naval Information Warfare Ctr. Pacific (United States)
Program Committee
- Tarek Abdelzaher, Univ. of Illinois (United States)
- Erik Blash, Air Force Office of Scientific Research (United States)
- Addison Bohannon, CCDC Army Research Lab. (United States)
- Kevin Chan, CCDC Army Research Lab. (United States)
- Supriyo Chakraborty, IBM Thomas J. Watson Research Ctr. (United States)
- Geeth del Mel, IBM United Kingdom Ltd. (United Kingdom)
- Tim Hanratty, CCDC Army Research Lab. (United States)
Program Committee continued...
- Brian J. Henz, CCDC Army Research Lab. (United States)
- Myron E. Hohil, CCDC Armament Ctr. (United States)
- Brian Jalaian, CCDC Army Research Lab. (United States)
- Nandi O. Leslie, CCDC Army Research Lab. (United States)
- Henry Leung, Univ. of Calgary (Canada)
- Gavin Pearson, Defence Science and Technology Lab. (United Kingdom)
- Alun D. Preece, Cardiff Univ. (United Kingdom)
- Peter Schwartz, Mitre (United States)
- Maggie B. Wigness, CCDC Army Research Lab. (United States)
- Robert Williams, Discovery Lab. Global (United States)
Call for
Papers
Papers
AI/ML is a foundational requirement for Multi-Domain Operations (MDO). To solve some of MDO’s most critical problems, the future force requires the ability to converge capabilities from across multiple domains at speeds and scales beyond human cognitive abilities. 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 urban settings, (ii) operate with extreme resource constraints (communications, computational, and size-weight-power), (iii) learn in complex data environments with small data samples, dirty data, high clutter, and deception; and (iv) rely on rapidly-adaptable teams of autonomous AI systems that interact and learn from human understanding of high-level goals. Most importantly, reliance by the warfighter on AI at the tactical edge will require AI that is reliable and safe, robust to adversarial attacks and adaptive to changing 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 not limited to the following:
The military has unique technical challenges that the commercial sector will not address as it will increasingly: (i) engage in distributed operations in complex urban settings, (ii) operate with extreme resource constraints (communications, computational, and size-weight-power), (iii) learn in complex data environments with small data samples, dirty data, high clutter, and deception; and (iv) rely on rapidly-adaptable teams of autonomous AI systems that interact and learn from human understanding of high-level goals. Most importantly, reliance by the warfighter on AI at the tactical edge will require AI that is reliable and safe, robust to adversarial attacks and adaptive to changing 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 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
- Virtual, Augmented & Mixed Reality (XR) Technology for Multi-Domain Operations (new)
- Unmanned Systems Technology XXII
- Disruptive Technologies in Information Sciences IV
This conference has an open call for papers:
submit an abstract
(sign in required)
Submission guidelines for Authors and Presenters
submit an abstract
(sign in required)
Submission guidelines for Authors and Presenters
