(SPIDERSENSE) II - Situationally Pertinent Information Detection and Relevance Sensing Engine

Complex military missions have failed in the past due to poor planning, wherein critical contextual elements of the mission environment, team, equipment, and/or contingencies were missed. Furthermore, important information is often lost in transition from planning to action stages in teamwork due to memory leaks and communication errors. The ultimate success of a mission relies on gathering and capturing critical contextual elements to build a shared situational awareness, a transactive memory system (TMS) to capture the gathered knowledge throughout the cycle of teamwork, and the ability to quickly re-instantiate a team when a loss or disruption has occurred. Although optimizing team transitions and formations are time-consuming and resource intensive, artificial social intelligence (ASI) agents can optimize the work by capturing, reasoning, and predicting over team and mission data to support complex transitions. To enable application of ASI to planning problems, SPIDERSENSE II will create interfaces that support collaborative team planning, generation of machine-readable plans, and flexible team formation approaches that incorporate ASI reasoning. These will be created as components connected to an existing human-agent team (HAT) testbed developed in the DARPA ASIST program, resulting in a testbed aligned to support research on critical operational problems.