Consider scenarios where such capabilities can enhance command and control during MDO:
A battlefield truck driver needs to give a status report to operations centers, from the strategic to the tactical support areas. What communications are available along the route? What onboard technology will feed into the report? Must the driver find a friendly command post or a commercial node to transmit his report? Is there a single channel, line-of-sight, or beyond line-of-sight option mounted to the truck? AI assured capabilities knows instantly and will automatically establish network connections, provide assured communications, and send the status report to the proper recipients.
The result is war fighters and command posts receive ‘on the move’ and ‘at the halt’ communications. AI assured capabilities also requires terrain mapping of potential command post locations, to include improved buildings and unimproved field locations that can support ‘at the halt’ and fixed communications requirements. AI assured capabilities overlays command post locations upon access points so warfighters can establish command posts at a location with the best network connectivity.
Marine Corps is experimenting with artificial intelligence to improve the way it deploys its forces and spot potential weaknesses years in advance.
The Marines built a tool that crunches data on personnel and equipment to measure how prepared individual battalions are for combat. The tool could ultimately help top brass deploy some 186,000 active-duty Marines and countless pieces of military hardware.
Allocating the service’s resources is an imperfect science. Leaders map out deployment strategies years or even decades in advance, but situations will invariably arise that throw a wrench in those plans.
Planners are constantly forced to “reshuffle the deck” as crises flare up in different places and figuring out which units to move around is a complicated process. Numerous factors—training, deployment history, equipment readiness and others—affect how prepared a group is for a given situation.
Today planners rely on spreadsheets, whiteboards and basic applications to track readiness and manage forces, but artificial intelligence can offer them a better understanding of the resources at their disposal and the long-term effects of the decisions they make.
The tech crunches both structured and unstructured data from multiple force management applications to create a real-time image of how prepared each unit is for combat. The tool specifically aims to build a five-year management plan for the Marine infantry battalions.
Tool has two primary functions: It flags the units that are most ready for action and explains why others come up short. Armed with that knowledge, commanders can proactively train and invest in less prepared groups before they fall even further behind.
“A lot of times Marines only invest more when the problem arises. Now they can see it ahead of time and say ‘OK, we’re going to take action now to prevent that from occurring.’”
The tool sheds light on how deployment decisions will affect forces in the long run. By analyzing historical trends along with real-time data, the tool could show how a unit’s readiness would change if it were, for instance, moved to a new location or given additional resources.
Marines are also building a separate AI system that ranks course of action plans based on those extrapolations, which could one day be merged with the readiness system.
“You integrate that all together and you get a full view of readiness across your force. Now you can really make some data-driven decisions.”
The next stage of the effort will include parts of the Marines’ aviation and logistics units, bringing about half branch into the purview of the program. With that additional data, the AI would further refine its processing rules to deliver better results.
So artificial intelligence is tasked with managing the particular deployments of troops in battle, moving them around in new and unexpected ways.
“Why would an AI allocate forces in distinct areas of the battlefield? It could intermingle them and manage them at a granular level. Its categories are way more numerous, in the way that a warehouse AI manages categories at the shelf level.
Instead of distinct groupings of armor, air support, infantry, and artillery, a system run by artificial intelligence and managing a battle could coordinate a single helicopter with a pair of howitzers and an infantry platoon, directly grouping each in the same way that a warehouse worker finds an assortment of items to place into the same package.
Objective is to develop and test a semi-autonomous wide area threat detection capability for site security using networked low-power, multi-modal signal acquisition nodes combined with machine learning for event classification and determination of hostile intent.
Our fixed sites face a wide area of security threats including traditional asymmetric weapons such as snipers, artillery, rockets & mortars, and more recently, the coordinated multi-agent “swarm” of low-cost drones. Other threats include physical incursion, vehicle-based improvised explosives, etc. Battlefield sensor technologies have been developed to provide threat reports to central command posts.
Typically, these systems are used to indicate the threat point-of-origin, and corresponding track if available. For area-target weapons, such as mortars, rockets, and RPGs, the point-of-impact locations may also be resolved. In many cases, the weapon type may be determined, which is vital to deploy countermeasures effectively.
Traditionally, sensor node locations are placed at fixed sites often constrained by availability of power and network communications. Once commissioned, surveillance systems are usually static, but the threat remains dynamic based on the conditions outside the fixed site and within the fixed site as critical assets are moved/relocated.
Using the capability of the multi-agent technology in networks could enhance most of the military applications like target tracking, urban control systems, firefighter assistant, data aggregation, detecting and monitoring the events, and intrusion detection. Mmulti agent systems have become an essential part of the real-world applications of networks like Swarm Sense robotics-based systems.
A common drone swarm system could consist of two drones unmanned aerial vehicle/over thousands of drones. The required autonomy increased to control such systems without any manual pilots, when the number of drones in a swarm system exceeded a predetermined threshold. Therefore, it is vital to create the autonomous drones which manage themselves automatically, effectively, and robustly in any anti-access, bandwidth-limited, and area-denied environments.
Due to the interconnection of multi agent systems and wireless networks, group of drones can be enabled to cooperate and coordinate them to perform the missions automatically, which require a large-area coverage, immediate data processing, efficient deployment without exact pre-planning, and uninterrupted cooperation and coordination during the emergency operations.
This project proposes technology for wide area surveillance in-and-around fixed sites providing mobile, semi-autonomous sensors which may be continually relocated/repositioned in lieu of changing threats or environmental factors.
Distributed artificial intelligence is concerned with interaction, especially coordination between agents exhibiting auto behaviour. Since distributed network solution strategies are spreading very rapidly due to tech advances, commanders have pressing needs for distributed techniques in mission readiness determination.
Advanced Battle Management System is a key technology the service is banking on to connect the information collected by various platforms into a complete picture of the battlespace to rapidly share data about a simulated attack.
That information, as well as other data from platforms participating in the exercise, was then pushed to a control command post where leaders could watch updates in real time.