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.
@article{arxiv.2010.14388,
title = {An Experimentation Platform for Explainable Coalition Situational Understanding},
author = {Katie Barrett-Powell and Jack Furby and Liam Hiley and Marc Roig Vilamala and Harrison Taylor and Federico Cerutti and Alun Preece and Tianwei Xing and Luis Garcia and Mani Srivastava and Dave Braines},
journal= {arXiv preprint arXiv:2010.14388},
year = {2020}
}
Comments
Presented at AAAI FSS-20: Artificial Intelligence in Government and Public Sector, Washington, DC, USA