Environment-driven Conceptual Learning (ECOLE)
Abstract
The Environment-driven Conceptual Learning (ECOLE) program is creating 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 aims to 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 newly observed object or action as a member of a previously-learned class, and to readily learn a new symbolic representation through interaction with its human partner.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2025
- Source ID
- 8b1661a4b39f137f9ff3d90955c63ed1