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Representing and reasoning over military context information in complex multi domain battlespaces using artificial intelligence and machine learning
Author(s): Gregory B. Judd; Claudia M. Szabo; Kevin S. Chan; Vanja Radenovic; Peter Boyd; Kelvin Marcus; Dale Ward
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Paper Abstract

In order to make sensible decisions during a multi domain battle, autonomous systems, just like humans, need to understand the current military context. They need to ‘know’ important mission context information such as, what is the commander’s intent and where are, and in what state, are friendly and adversary actors. They also need an understanding of the operating environment; the state of the physical systems ‘hosting’ the AI; and just as importantly, the state of the communication networks that allows each AI ‘node’ to receive and share critical information. The problem is: capturing, representing, and reasoning over this contextual information is especially challenging in distributed, dynamic, congested and contested multi domain battlespaces. This is not only due to rapidly changing contexts and noisy, incomplete and potentially erroneous data, but also because, at the tactical edge, we have limited computing, storage and battery resources. The US Army Research Laboratory, Australia’s Defence Science Technology Group and associated University partners are collaborating to develop an autonomous system called SMARTNet that can transform, prioritize and control the flow of information across distributed, intermittent and limited tactical networks. In order to do this however, SMARTNet requires a good understanding of the current military context. This paper describes how we are developing this contextual understanding using new AI and ML approaches. It then describes how we are integrating these approaches into an exemplar tactical network application that improves the distribution of information in complex operating environments. It concludes by summarizing our results to-date and by setting a way forward for future research.

Paper Details

Date Published: 10 May 2019
PDF: 15 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100607 (10 May 2019); doi: 10.1117/12.2518580
Show Author Affiliations
Gregory B. Judd, Defence Science and Technology Group (Australia)
Claudia M. Szabo, The Univ. of Adelaide (Australia)
Kevin S. Chan, U.S. Army Research Lab. (United States)
Vanja Radenovic, Defence Science and Technology Group (Australia)
Peter Boyd, Defence Science and Technology Group (Australia)
Kelvin Marcus, U.S. Army Research Lab. (United States)
Dale Ward, Consilium Technology (Australia)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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