An Experimentation Platform for Explainable Coalition Situational Understanding
This work addresses the need for explainable and interoperable AI systems in coalition military operations, though it appears incremental as it focuses on platform development rather than novel algorithmic breakthroughs.
The paper introduces the Situational Understanding Explorer (SUE) platform, a lightweight and open experimentation tool designed to support coalition multi-domain operations by integrating explainable AI/ML and symbolic-subsymbolic approaches for event processing in dense urban terrain.
We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing. The Situational Understanding Explorer (SUE) platform is designed to be lightweight, to easily facilitate experiments and demonstrations, and open. We discuss our requirements to support coalition multi-domain operations with emphasis on asset interoperability and ad hoc human-machine teaming in a dense urban terrain setting. We describe the interface functionality and give examples of SUE applied to coalition situational understanding tasks.