Assured Autonomy (DARPA)
Abstract: The goal of the Assured Autonomy program is to create technology for continual assurance of Learning-Enabled, Cyber Physical Systems (LE-CPSs). Continual assurance is defined as an assurance of the safety and functional correctness of the system provided provisionally at design time, and continually monitored, updated, and evaluated at operation-time as the system and its environment evolves. An LE-CPS is defined as a system composed of one or more Learning-enabled Components (LECs). A LEC is a component whose behavior is driven by “background knowledge” acquired and updated through a “learning process,” while operating in a dynamic and unstructured environment. This definition generalizes and admits a variety of popular machine learning approaches and algorithms (e.g., supervisory learning for training classifiers, reinforcement learning for developing control policies, algorithms for learning system dynamics). The generalization is intentional to promote abstractions and tools that can be applied to different types and applications of data-driven machine learning algorithms in Cyber Physical Systems (CPSs) to enhance their autonomy. In order to ground the Assured Autonomy research objectives, the program will prioritize challenge problems in the militarily relevant autonomous vehicle space. However, it is anticipated that the tools, toolchains, and algorithms created will be relevant to other LE-CPSs.