Defense Advanced Research Projects AgencyTagged Content List


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

Showing 17 results for Algorithms + Autonomy RSS
DARPA published its Young Faculty Award (YFA) 2018 Research Announcement today, seeking proposals in 26 different topic areas—the largest number of YFA research areas ever solicited.
DARPA has successfully completed its Anti-Submarine Warfare (ASW) Continuous Trail Unmanned Vessel (ACTUV) program and has officially transferred the technology demonstration vessel, christened Sea Hunter, to the Office of Naval Research (ONR). ONR will continue developing the revolutionary prototype vehicle—the first of what could ultimately become an entirely new class of ocean-going vessel able to traverse thousands of kilometers over open seas for months at a time, without a single crew member aboard—as the Medium Displacement Unmanned Surface Vehicle (MDUSV).
Machine learning (ML) systems today learn by example, ingesting tons of data that has been individually labeled by human analysts to generate a desired output. As these systems have progressed, deep neural networks (DNN) have emerged as the state of the art in ML models. DNN are capable of powering tasks like machine translation and speech or object recognition with a much higher degree of accuracy. However, training DNN requires massive amounts of labeled data–typically 109 or 1010 training examples. The process of amassing and labeling this mountain of information is costly and time consuming.
A key ingredient in effective teams – whether athletic, business, or military – is trust, which is based in part on mutual understanding of team members’ competence to fulfill assigned roles. When it comes to forming effective teams of humans and autonomous systems, humans need timely and accurate insights about their machine partners’ skills, experience, and reliability to trust them in dynamic environments. At present, autonomous systems cannot provide real-time feedback when changing conditions such as weather or lighting cause their competency to fluctuate. The machines’ lack of awareness of their own competence and their inability to communicate it to their human partners reduces trust and undermines team effectiveness.
Current AI systems excel at tasks defined by rigid rules – such as mastering the board games Go and chess with proficiency surpassing world-class human players. However, AI systems aren’t very good at adapting to constantly changing conditions commonly faced by troops in the real world – from reacting to an adversary’s surprise actions, to fluctuating weather, to operating in unfamiliar terrain. For AI systems to effectively partner with humans across a spectrum of military applications, intelligent machines need to graduate from closed-world problem solving within confined boundaries to open-world challenges characterized by fluid and novel situations.