Environment-driven Conceptual Learning (ECOLE)
Funding Agency:
- Department of Defense
The United States Department of Defense (DoD) and Intelligence Community (IC) need computational systems that can robustly and automatically analyze large amounts of multimodal data. Furthermore, these computational systems need to be able to communicate and cooperate with human beings to resolve ambiguities and improve performance over time. The Environment-driven Conceptual Learning (ECOLE) Program will create AI agents capable of continually learning from linguistic and visual input to enable human-machine collaborative analysis of image, video, and multimedia documents during time-sensitive, mission-critical DoD analytic tasks, where reliability and robustness are essential. ECOLE will transform current Machine Learning approaches by developing algorithms that can identify, represent, and ground the attributes that form the symbolic and contextual model for a particular object or activity through interactive learning with a human analyst. Knowledge of attributes and affordances, learned dynamically from data encountered within an analytic workflow, will enable joint reasoning with a human partner. This acquired knowledge will also enable the machine to recognize when an observed object or activity is novel, rather than misclassifying the newlyobserved objection or action as a member of a previously-learned class, and readily learn a new symbolic representation through interaction with its human partner. Attribute-informed novelty detection will also enable the machine to detect changes in known objects and report these changes when they are significant.
Multiple awards are anticipated.
Abstract Due Date and Time: September 29, 2022 at 12:00 noon, Eastern Time
November 14, 2022 at 12:00 noon, Eastern Time
The BAA Coordinator for this effort can be reached at: ECOLE@darpa.mil