Summaries - Office of Research & Innovation
Back Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization
|Division||Research & Sponsored Programs|
|Department||Naval Research Program|
Proulx, Ronald J.
Ross, Isaac M.
Magallanes, Lara C
|Sponsor||NPS Naval Research Program (Navy)|
|Summary||This study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate ¿sensor¿ for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.|
|Publications||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|
|Data||Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal|