For NASA's planned manned exploration class missions, task and psycho-social performances can be subject to degradation due to extended periods of isolation in extreme environments, social monotony, and other factors related to long duration missions. Utilizing research in socio-linguistics theory, AD ASTRA provides non-intrusive and automated methods for analyzing textual data produced by these isolated teams to gain insight into a group's team dynamic and individual psychosocial states.
ANSIBLE is a multi-modal toolset that can be used pre, during, and post flight to connect a flight crew with their family, friends, and the ground crew to provide a sense of social consistency and permanence. ANSIBLE 1) adapts, rearranges, and modifies human interaction streams to minimize the disruptive impact of communication latencies and 2) leverages virtual worlds (VW) to provide a space where humans and intelligent virtual agents (VA) can be companions, advisors, provided psychological support, and share experiences.
Our approach to construct a UI design aid for NASA—called the Multi-modal Aid for Interface Design (MAID), targeted towards designing displays consistent with the procedural requirements used in space-based missions, such as Shuttle and ISS.
In April of 2004, SIFT and Dr. Lewis Johnson of the University of Southern California (USC)’s CARTE labs, proposed and won Phase I of DARPA SBIR SB041-009 (Contract #W31P4Q-04-C-R221, DARPA Program Manager: Dr. Ralph Chatham, 703-696-7501) titled “The Etiquette Quotient: Evaluating Social Skills in Conversational Avatars.” This work developed a “believability metric” for assessing and predicting when a given level of etiquette behavior exhibited by a computerized avatar or non-player character (NPC) in a game or simulation would be perceived as “unbelievable” by a human player or observer.
This demonstration video, prepared by the U.S. Army's Aeroflight Dynamics Directorate, illustrates a version of SIFT's PLaybook interacting with the Army's Multiple UAV Simulation Environment (MUSE) in realistic commanding of multiple, heterogenous UAVs for rapid prosecution of a critical target.