An Experimentation Platform for Explainable Coalition Situational Understanding

Abstract We present an experimentation platform for coalition situa- tional understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML ap- proaches for event processing. The Situational Understanding Explorer (SUE) platform is designed to be lightweight, to eas- ily 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.
  • Katie Barrett-Powell (Cardiff)
  • Jack Furby (Cardiff)
  • Liam Hiley (Cardiff)
  • Marc Vilamala (Cardiff)
  • Harrison Taylor (Cardiff)
  • Federico Cerutti (Cardiff)
  • Alun Preece (Cardiff)
  • Tianwei Xing (UCLA)
  • Luis Garcia (UCLA)
  • Mani Srivastava (UCLA)
  • Dave Braines (IBM UK)
Date Nov-2020
Venue AAAI 2020 Fall Symposium Series [link]