Defense Advanced Research Projects AgencyTagged Content List

Harnessing Complexity

Systems comprising multiple and diverse interactions

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Developers of imaging systems have long been beholden to certain rules of optics designs so well established and seemingly immutable as to be treated as virtual “laws” of physics. One widely considered pillar of optical design, for example, is that imaging systems must be built from a series of complex and precisely manufactured optical elements arranged linearly. The result of such assumptions is that certain high-performance imagery devices inevitably end up being large and heavy, composed of dozens or more optical elements.
The social sciences can play important roles in assisting military planners and decision-makers who are trying to understand complex human social behaviors and systems, potentially facilitating a wide range of missions including humanitarian, stability, and counter-insurgency operations. Current social science approaches to studying behavior rely on a variety of modeling methods—both qualitative and quantitative—which seek to make inferences about the causes of social phenomena on the basis of observations in the real-world.
DARPA’s Defense Sciences Office (DSO), which identifies and pursues high-risk, high-payoff research initiatives across a broad spectrum of science and engineering disciplines, will host Discover DSO Day (D3) on June 15, in Arlington, Virginia. The event is designed to familiarize potential proposers with the mission, research areas of interest, and business processes pursued by the DSO, a fundamental research office with a history of not only reshaping existing technical fields but also creating entirely new disciplines—and of transforming bold, paradigm-challenging initiatives into game-changing technologies for U.S. national security.
Computational models and simulations can be enormously helpful when designing complex military systems such as new aerospace vehicles and engines, reducing development costs and times. However, realistic, high-fidelity models require enormous amounts of computing power in order to be able to accommodate all of the different factors that may affect predictive accuracy. To mitigate this computational cost, researchers often use simplified models, but these models contain assumptions, ambiguities, incomplete information, and inputs that vary unpredictably.
Advances in artificial intelligence (AI) are making virtual and robotic assistants increasingly capable in performing complex tasks. For these “smart” machines to be considered safe and trustworthy collaborators with human partners, however, robots must be able to quickly assess a given situation and apply human social norms. Such norms are intuitively obvious to most people—for example, the result of growing up in a society where subtle or not-so-subtle cues are provided from childhood about how to appropriately behave in a group setting or respond to interpersonal situations. But teaching those rules to robots is a novel challenge.