Coalition situational understanding for multi-domain operations (Conference Presentation)

Coalition situational understanding (CSU) is fundamental to support decision-making and autonomy in multi-domain operations involving multiple allied partners. Our work aims to advance the algorithms and techniques to develop CSU, addressing key scientific challenges in how different levels of representation, reasoning and machine learning (ML) interact with one another to facilitate flow of information and management of uncertainty between coalition agents and services. The very existence of a coalition is contingent on the premise that the whole is greater than the sum of the parts, i.e., the shared model of the environment - acquired using the information learned, combined and inferred from all the agents - is not only more complete but also more robust than local models. Specifically, two aspects of achieving this CSU vision are considered in this paper: (1) integrating learning and reasoning techniques for CSU, addressing the technical challenge of dealing with uncertainly in the