Judd, G. B., Szabo, C. M., Chan, K. S., Radenovic, V., Boyd, P., & Marcus, K., et al.. 2019, Representing and reasoning over military context information in complex multi domain battlespaces using artificial intelligence and machine learning. Paper presented at Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications.
|Resource type: Proceedings Article
BibTeX citation key: Judd2019
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|Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, General, Geopolitical, Military Science
Subcategories: Army, Australia, Command and control, Decision making, Doctrine, Edge AI, Fog computing, Human decisionmaking, JADC2, Machine learning, Military research, Mosaic warfare, Networked forces, Neural nets, Psychology of human-AI interaction
Creators: Boyd, Chan, Judd, Marcus, Radenovic, Szabo, Ward
Publisher: International Society for Optics and Photonics
Collection: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
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.