Defense Advanced Research Projects AgencyTagged Content List


A process or rule set used for calculations or other problem-solving operations

Showing 34 results for Algorithms + Programs RSS
In a target-dense environment, the adversary has the advantage of using sophisticated decoys and background traffic to degrade the effectiveness of existing automatic target recognition (ATR) solutions. Airborne strike operations against relocatable targets require that pilots fly close enough to obtain confirmatory visual identification before weapon release, putting the manned platform at extreme risk. Radar provides a means for imaging ground targets at safer and far greater standoff distances; but the false-alarm rate of both human and machine-based radar image recognition is unacceptably high. Existing ATR algorithms also require impractically large computing resources for airborne applications.  
New manufacturing technologies such as additive manufacturing have vastly improved the ability to create shapes and material properties previously thought impossible. Generating new designs that fully exploit these properties, however, has proven extremely challenging. Conventional design technologies, representations, and algorithms are inherently constrained by outdated presumptions about material properties and manufacturing methods. As a result, today’s design technologies are simply not able to bring to fruition the enormous level of physical detail and complexity made possible with cutting-edge manufacturing capabilities and materials.
The Understanding Group Biases (UGB) program seeks to develop and prove out capabilities that can radically enhance the scale, speed, and scope of automated, ethnographic-like methods for capturing group biases and cultural models from increasingly available large digital datasets.
Successful integration of next generation AI into DoD applications must be able to deal with incomplete, sparse and noisy data as well as unexpected circumstances that might arise while solving real world problems. Thus, there is a need for new computing models that are efficient and robust, can learn new concepts with very few examples, and can guide the development of adequate novel hardware to support them.
| AI | Algorithms | Math |