1. Laboratory Overview MIT Humans and Automation Lab February 2006

2. Overview of the MIT Humans and Automation Laboratory 2 History Created in 2004 Director: Dr. Mary (Missy) Cummings Post Doctorate Associates: Dr. Stacey Scott, Dr. Jake Crandall Students: Sylvain Bruni, Jessica Marquez, Carl Nehme, Yves Boussemart, Amy Brzezinski, Angela Ho, Cristin Smith, Chris Tsonis, Patrick Laney, Jim McGrew and an army of undergraduate researchers!

3. Overview of the MIT Humans and Automation Laboratory 3 HAL Director Former U.S. Navy officer and pilot Systems engineer with a cognitive focus Research Interests: Human supervisory control, decision support design, human interaction with autonomous systems, design of experiments technology development, social impact of technology

4. Overview of the MIT Humans and Automation Laboratory 4 Research and Current Projects Research in the Humans and Automation Lab (HAL) focuses on the multifaceted interactions of human and computer decision-making in complex sociotechnical systems. Time-Sensitive Operations for Distributed Teams Human Supervisory Control Issues of Multiple Unmanned Vehicles Reduced manning Measurement of Display Complexity Collaborative Human Computer Decision Making Integrated Sensor Decision Support Sponsors: Office of Naval Research, NASA, Boeing, U.S. Army, FAA, Lincoln Labs, AAI.

5. Overview of the MIT Humans and Automation Laboratory 5 Task Displays Human Operator (Supervisor) Computer Actuators Sensors Controls Human Supervisory Control Humans on the loop vs. in the loop Supporting knowledge-based versus skill-based tasks Network-centric operations & cognitive saturation

6. Overview of the MIT Humans and Automation Laboratory 6

7. 7 Boeing & the MIT Humans and Automation Laboratory Collaborative Time Sensitive Targeting 2005-2009 Phantom Works, Seattle Mary Hornsby, Paul Parks, (formerly Dave Graeber) Activity awareness technology development Supervisor workload management support Team transition/handoff support Upcoming conference paper at CCRTS in San Diego Teaming with AFRL Human Supervisory Control Issues in Network Centric Warfare Multi-UAV control test bed 2 journal articles (one selected as top ten of conference) 1 book chapter 5 conference papers

8. Overview of the MIT Humans and Automation Laboratory 8 Collaborative Time-Sensitive Operations for Distributed Teams What aspects of collaborative TST would be best served by collaborative technologies? Can we develop technology to facilitate collaborative decision-making in time-critical settings?

9. Overview of the MIT Humans and Automation Laboratory 9 Supporting Supervisors of Teams of HSC Operators Supporting the Control of Teams of Heterogeneous Unmanned Vehicles

10. Overview of the MIT Humans and Automation Laboratory 10 Human Supervisory Control Issues in Network Centric Warfare How do operators manage multiple supervisory control processes? What effects do human performance limitations have on the overall system? Mitigating workload Reduced Manning Combining Shadow positions Multiple UV control for a single operator Boeing, Mitre, Lincoln Labs

11. Overview of the MIT Humans and Automation Laboratory 11 Decision -Making Collaborative Human Computer Decision Making Three projects: TLAM Mission Planning Tool Decision Support for Planetary Exploration Decision Support for control of multiple UAVs Both humans and computers bring different strengths and limitation to problems solving in large problems spaces with many variables, some changing dynamically Determine how humans and computer optimization algorithms can complement each other Decision- Making

12. Overview of the MIT Humans and Automation Laboratory 12 Collaborative Human Computer Decision Making Tomahawk Strike Planning: missions and missiles Integration of temporal & spatial elements, resource allocation, managing assets, and performing contingency planning in a high workload environment. Cognitive Strategies

13. Overview of the MIT Humans and Automation Laboratory 13 Managing Multiple UAVs Replanning under time pressure with “intelligent” autonomy Multivariate optimization problem with no real optimal solution How much and where should automation be inserted?

14. Overview of the MIT Humans and Automation Laboratory 14 Collaborative Human Computer Decision Making Decision Support for Planetary Exploration Astronaut traversal replanning task Path replanning with increasingly complex constraints Affect of level of automated decision support

15. Overview of the MIT Humans and Automation Laboratory 15 Measurement of ATC Display Complexity Air Traffic Control display complexity metrics. Ran experiments to understand the effect of specific complexity factors (color, # aircraft, structure, environmental change). To better understand various scales of complexity in the ATC system. Environmental, organizational, interface, & cognitive complexity Quantifying display complexity. Develop design guidelines for ATC displays. Relevance to future UAV operations

16. Overview of the MIT Humans and Automation Laboratory 16 Thank you! missyc@mit.edu http://halab.mit.edu