Representing and reasoning over military context information in complex multi domain battlespaces using artificial intelligence and machine learning

摘要

In order to make sensible decisions during a multi domain battle, autonomous systems, just like humans, need tounderstand the current military context. They need to ‘know’ important mission context information such as, what is thecommander’s intent and where are, and in what state, are friendly and adversary actors. They also need an understandingof the operating environment; the state of the physical systems ‘hosting’ the AI; and just as importantly, the state of thecommunication 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, congestedand contested multi domain battlespaces. This is not only due to rapidly changing contexts and noisy, incomplete andpotentially erroneous data, but also because, at the tactical edge, we have limited computing, storage and batteryresources. The US Army Research Laboratory, Australia’s Defence Science Technology Group and associatedUniversity partners are collaborating to develop an autonomous system called SMARTNet that can transform, prioritizeand control the flow of information across distributed, intermittent and limited tactical networks. In order to do thishowever, SMARTNet requires a good understanding of the current military context. This paper describes how we aredeveloping this contextual understanding using new AI and ML approaches. It then describes how we are integratingthese approaches into an exemplar tactical network application that improves the distribution of information in complexoperating environments. It concludes by summarizing our results to-date and by setting a way forward for futureresearch.