Situation Theory Based Query Generation for Determining Situation Awareness Across Distributed Data Streams

While conventional measures of Situation Awareness (SA) focus on its adequacy in supporting decisions, fully exploiting information being returned by teams of unmanned aerial vehicles (UAVs) requires close attention to the sources as well as the impact of information. The problem of measuring SA for UAV teams lies in determining the extent to which information is being integrated from across the team. To address this problem, we are developing a Situation Theory based method for spatially and temporally indexing relevant situations. Observations from each UAV are treated as 'infons' indexed by their particular time and place. By maintaining provenance on individual observations, we can trace their participation in supporting operator SA and use these data to evaluate the effects of alternative data presentations for multi-UAV surveillance. A wildfire monitoring application domain is introduced, and an example developed from a wildfire training scenario for Great Sand Dunes National Park.