DARPA seeks proposals for CAML program
On February 19, the Defense Advanced Research Projects Agency posted a solicitation for the Competency-Aware Machine Learning (CAML) program. Abstracts are due by 4:00 p.m. Eastern on March 6, and full proposals are due by 4:00 p.m. Eastern on April 22.
The Defense Sciences Office (DSO) at the Defense Advanced Research Projects Agency (DARPA) is soliciting innovative research proposals in the area of competency-awareness machine learning, whereby an autonomous system can self-assess its task competency and strategy and express both in a human-understandable form. This competency-awareness capability contributes to the goal of transforming autonomous systems from tools into trusted, collaborative partners. The resulting competency-aware machine learning systems will enable machines to control their behaviors to match user expectations and allow human operators to quickly and accurately gain insight into a systemâ€™s competence in complex, time-critical, dynamic environments. The Competency-Aware Machine Learning (CAML) program will, in this way, improve the efficiency and effectiveness of human-machine teaming. Proposed research should investigate innovative approaches that enable revolutionary advances in science. DSO will exclude proposals that propose evolutionary improvement to the existing state of practice.
The goal of CAML is to develop a competency-aware machine learning framework to support transitioning machine learning systems from tools into trusted partners. Achieving this goal requires the development of new elements in machine learning, including memory mechanisms, knowledge abstraction and representation, and behavior self-modeling.
CAML is interested in a diverse range of DoD-relevant machine learning problems including, but not limited to, learning systems for object recognition, robotic navigation, action planning, and decision-making. Developed technologies will be tested using realistic test vignettes with actual applications. Initial testing will be performed on machine learning platforms and applications chosen by the proposer, while final tests will involve Government experimental platforms and applications.
CAML will evaluate the accuracy of the machine’s communication of its competency, not the human-perception of the machine’s competency. Methodologies developed by proposers should be generalizable and applicable to a broad class of machine learning systems. Handcrafted solutions tailored to specific machine learning applications are not of interest. Since the emphasis is on a competency-aware framework, the development of new machine learning platforms is outside of the program scope.
Full information is available here.