An Experimentation Platform for Explainable Coalition Situational Understanding
Katie Barrett-Powell, Jack Furby, Liam Hiley, Marc Roig Vilamala, Taylor, Harrison, Federico Cerutti, Alun D. Preece, Tianwei Xing, Garcia, Luis, Mani B. Srivastava, Dave Braines · arXiv (Cornell University) · 2020
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.