Photo By Debora Henley | U.S. Air Force Col. Jonathan Zall, Department of Air Force Advanced Battle Management...... read more read more
The Department of the Air Force’s Advanced Battle Management System Cross-Functional Team hosted the first software sprint experiment to examine software solutions for enhanced command and control in conjunction with the Shadow Operations Center – Nellis, at the Howard Hughes Operations, or H2O, facility in Las Vegas, Nevada, Sept. 9-13, 2024.
Eight industry software teams, along with one military development team from the ShOC-N’s Innovation Directorate, were asked to create and refine solutions against the Generating Battle Course of Actions problem set, which is designed to outline comprehensive battlespace information and decision-making tasks for human-machine teams in battle management.
The Air Force Research Laboratory Information Directorate invited industry partners to the first Generating Battle COAs “sprint week” to support the DAF BATTLE NETWORK effort to facilitate faster and better decisions by battle managers when engaging targets. The DAF BATTLE NETWORK is the U.S. Air Force’s contribution to the Combined Joint All-Domain Command and Control initiative.
The goal of the CJADC2 concept is to enable data sharing and data-informed decision making across all branches of service, our allies and partners, which is possible because of redundantly and robustly connected networks moving data from sensors to shooters across a distributed network, making information accessible anywhere, anytime.
“To win against a peer adversary, the joint force must achieve decision advantage by equipping HMTs with automation to sort through complex and high-volume battle management decisions with a faster tempo and improved decision quality,” said Col. Christopher Cannon, U.S. Air Force team lead, ABMS CFT.
Generating Battle COAs, or GBC, today is almost entirely done through human decision making. Current HMTs involve numerous personnel completing the GBC decision function while using software that merely presents information versus automating faster and better-informed battle COAs.
Software coders used the ABMS CFT’s Transformational Model for decision-making to develop tools explicitly aimed at decision advantage, which is the ability to make better and faster decisions than an opponent. Current DAF C2 systems and processes are optimized for low-intensity conflict and do not address decision advantage over a high-intensity pacing threat.
The teams interacted with DAF battle managers and operators throughout the sprint, receiving feedback on their human-machine interfaces while they refined algorithms that machine-assist human decision-making.
“The goal is to compare decision performance against baseline [problem sets] and provide measurable changes in decision performance of the HMT,” said Cannon.
During the first three days of sprint week, software teams provided quick iteration and refinement of solutions to the GBC problem set. Battle managers and operators examined each team’s solution to the problem set(s) during the final two days of the experiment.
“Some of the teams, by the end of sprint week, were able to produce a software microservice that made the decision for the human, which is a big deal for us because this puts us on a path to using Col. Zall’s [ABMS Capability Integration Chief] model to code software that creates the machine teammate for the human battle manager and this week helped us validate that,” said Cannon.
“The ShOC will look at ways to incorporate the code as it develops and gains traction into future experiments in long-range kill chain development to get after these complicated battle management problem sets,” said Maj. Wesley Schultz, 805th Combat Training Squadron/ShOC-N director of operations, Nellis Air Force Base, Nevada.
ABMS CFT members considered the HMT solutions’ speed, correctness, completeness, and user experience and the software services’ ability to measure utility, cost, and risk.
“When we looked at the completeness, meaning was the machine able to account for all of the assets from start to finish, did it take into account all the possible effectors and choices available because we don’t want the machine to ignore good options for the human that are physically possible and tactically sound,” said Cannon. “Our initial analysis at the end of the week, so far indicates that results certainly exceeded human ability, but we don’t yet have fidelity on all the data to make any final assertions.”
About Advanced Battle Management System Cross-Functional Team
The ABMS CFT provides operational requirements for the architecture, digital infrastructure, and software for the DAF BATTLE NETWORK, compresses C2 planning and execution to help speed up decision-making, and identifies capability improvements through battle management experiments.
About Air Force Research Laboratory
The AFRL is the primary scientific research and development center for the DAF. AFRL plays an integral role in leading the discovery, development, and integration of affordable warfighting technologies for air, space, and cyberspace forces.
About Shadow Operations Center - Nellis
The 805th Combat Training Squadron is the home of the ShOC-N, the U.S. Air Force’s primary ABMS Battle Lab for experimentation and incubation of new command and control technologies and development of C2 tactics, techniques, and procedures enabling multi-echelon, multi-domain battle management.
| Date Taken: | 10.16.2024 |
| Date Posted: | 10.17.2024 12:15 |
| Story ID: | 483336 |
| Location: | LAS VEGAS, NEVADA, US |
| Web Views: | 37 |
| Downloads: | 0 |
This work, Optimizing decision advantage through human-machine team experimentation, by Debora Henley, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Command and Control
