Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization
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Authors
Karpenko, Mark
Ross, Isaac M.
Proulx, Ronald J.
Magallanes, Lara C.
Subjects
Artificial Intelligence
Autonomy
Great Power Competition
ISR
Communications
Human-Machine Teaming
Manned-Unmanned Teaming
Peer Adversary
Autonomy
Great Power Competition
ISR
Communications
Human-Machine Teaming
Manned-Unmanned Teaming
Peer Adversary
Advisors
Date of Issue
2022
Date
2022
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
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.
Type
Poster
Description
NPS NRP Project Poster
Series/Report No
Naval Research Program (NRP) Project Documents
Department
Mechanical and Aerospace Engineering (MAE)
Mechanical and Aerospace Engineering (MAE)
Organization
Naval Research Program (NRP)
Identifiers
NPS Report Number
Sponsors
Naval Special Warfare Command (NAVSPECWARCOM)
N9 - Warfare Systems
N9 - Warfare Systems
Funder
This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrp
Chief of Naval Operations (CNO)
Chief of Naval Operations (CNO)
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.