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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., Comment: Presented at AAAI FSS-20: Artificial Intelligence in Government and Public Sector, Washington, DC, USA

The use of paralytic medications in the performance of RSI intubation is a high risk intervention used by many HEMS crews. There is no margin for error in RSI intubation as the results can be fatal. Operating room access for airway management training has become more difficult, and is not representative of the environment in which HEMS crews typically function. LifeEvac of Virginia designed and implemented an SST airway management program to provide a realistic, consistent training platform. The dynamic program incorporates standardized scenarios, and real life challenging cases that this and other programs have encountered. SST is done in a variety of settings including the helicopter, back of ambulances, staged car crashes and simulation centers. The result has been the indoctrination of a well defined, consistent approach to every airway management intervention. The SST program facillitates enhancement of technical skills. as well as team dynamics and communication.