“We are going to be doing table top war games to see what kinds of things we will need in our platforms to counter threats,”
Wargames pit friendly “blue” teams against “red” teams acting like major adversaries. The exercise can involve maps, intelligence data, terrain and geographic factors as well as specifics regarding whatever populations or countries are involved. They are often literally on a table top with nearby computers, simulations and methods of data analysis, or in some cases they can be as large as moving structures on the floor of a gymnasium.
“Through a series of structured questions, you have leaders make decisions. In a table top you get reactions. You may even have players that act like a local population. You put it in context of geopolitical and tactical circumstance.
By emphasising that these wargames would be designed to identify enemy threats, its important to capture, replicate or precisely mirror weapons, tactics, strategies and force structure of specific high-tech adversaries.
While never exact, these kinds of wargames strive to closely approximate specific weapons systems, armored vehicles, missiles and drones a particular enemy force would have.
“If you can do it based on real-world intel, then you can do it on grounded projections.”
What would it look like if a “red” force launched a massive air-ground invasion? The blue team would consist of Combatant Commanders making decisions in response, taking adversary tanks, missiles, drones and weapons into account.
“How many combat vehicles does an enemy have? Or let’s say an enemy has a bullet that goes 1000 miles - let’s wargame what that means. Just because their weapons may be able to shoot a certain range, that does not mean they can hit a target. What kinds of sensors do they have? What kind of network?“
Unlike computer simulations which rely have more specifics embedded, wargames better address situations with more unknowns and assumptions. Both wargames and simulations are designed to test an ability to respond to specific actions and attacks made by a “red” force playing the enemy role
“Wargames include evidence based analytics. Eventually you have to apply experience. You cannot simply opine around a table.
For example, what should the response be if the adversary closes off a port, cuts of supply lines or uses air assets to destroy long range weapons? The amount of equipment, details about weapons capability and the exact configuration of an adversary force are replicated by the wargame.
Wargaming offers distinct models for decision-making. “One model is based upon adversary capabilities and the other on adversary intentions.
Battle damage assessments are of great significance in wargames. For instance, what happens if large portions of a force are wiped out by an attacking force? What kinds of options does that leave a commander?
The wargames are designed to encompass the full range of factors.
Losing a wargame is seen as a great development.
“If the red team outdoes you or comes up with something you are not prepared for, that’s a successful wargame because you want a realistic adversary. In this case you learn something you did not know.”
Navy is expanding its attack submarine war game strategy to further emphasise enhanced “spy” missions like intelligence, surveillance reconnaissance missions to quietly patrol shallow waters near coastline - scanning for adversary enemy submarines, surface ships and coastal threats.
Improved undersea navigation and detection technology, using new sonar, increased computer automation and artificial intelligence, enable quieter, faster movements in littoral waters where enemy mines, small boats and other threatening assets often operate.
Virginia-Class submarines are engineered with a “Fly-by-Wire” capability which allows the ship to quietly linger in shallow waters without having to surface or have each small move controlled by a human operator.
With “Fly-by-Wire” technology, a human operator will order depth and speed, allowing AI tools to direct the movement of the planes and rudder to maintain course and depth.
The ships can be driven primarily through AI code and electronics, thus freeing up time and energy for an operator who does not need to manually control each small manoeuvre. Previous Los Angeles-Class submarines rely upon manual, hydraulic controls.
This technology, using upgradable and fast-growing AI applications, widens the mission envelope for the attack submarines by vastly expanding their ISR potential. Using real-time analytics and an instant ability to draw upon an organize vast data-bases of information and sensor input, computer algorithms can now perform a range of procedural functions historically performed by humans to increase speed of manoeuvre and an attack submarine's ability to quickly shift course, change speed or alter depth positioning when faced with attacks.
A closer-in or littoral undersea advantage, Navy strategy documents explain, can increase “ashore attack” mission potential along with ISR-empowered anti-submarine and anti-surface warfare operations.
“We are uniquely capable of, and often best employed in, stealthy, clandestine and independent operations-- we exploit the advantages of undersea concealment which allow us to conduct undetected operations such as strategic deterrent patrols, intelligence collection, Special Operations Forces support, non-provocative transits, and repositioning.”
The Navy is implementing elements of this strategy with its recently launched Virginia-Class attack submarine engineered with a host of new, unprecedented undersea technologies.
Many innovations are underway and tested as prototypes for many years and are now operational as new subs enter service; service technology developers have, in a general way, said the advances in undersea technologies built, integrated, tested and now operational to include quieting technologies for the engine room to make the submarine harder to detect, a new large vertical array and additional "quieting" coating materials for the hull.
While firepower and attack weapons are naturally still a major area of focus for Virginia-Class submarines, the expanding ISR mission scope made possible by new technologies has provided key inspiration for senior Navy developers and members of Congress who have been working vigorously to increase the size of the attack submarine fleet.
Land weapons, port activities and other adversary movements in coastal or island areas are more difficult for deeper draft surface ships to access, often complicating surveillance missions – without giving away their position. Surface ships and the drones or aircraft they operate could, in a variety of operational environments, be more “detectable” to adversary radar and sensors when compared to attack submarines.
“The most important feature for manoeuvre in littoral waters is the fly-by-wire control system, whereby computers in the control center electronically adjust the submarine's control surfaces, a significant improvement from the hydraulic systems used in legacy subs.
Next-generation sonar technology, woven into Virginia-Class subs, is engineered to work in tandem with “Fly-by-Wire” technology to better identify threats operating at various depths and speed.
The new submarines also have what’s called a Large Aperture Bow conformal array sonar system – designed to listen for an acoustic ping, analyze the return signal, and provide the location and possible contours of enemy ships, submarines and other threats.
To understand autonomous weapons, think about electronic warfare. Parsing what detected signals matter is an electronic warfare skill that can possible be designated to artificial intelligence.
For as much as conflicts have been defined by drones and drone strikes, those missions are only possible because the sky is empty of hostile aircraft, and because the electromagnetic spectrum is free of interference. This permissiveness, however, is hardly a guarantee in the future and even in certain theaters in the present.
If the machines that are today remotely operated are to take part in future conflicts, they will need to operate on their own, with only minimal human control. The technologies that will make that possible are broadly grouped together under the subject of autonomy,
Automated defenses are used to counter automated attacks and speeds faster and scales larger than humans can work on their own. There is autonomy in guided munitions, especially loitering weapons, which operate on their own from launch until impact. Waging electronic warfare will require both approaches: machines that can automatically counter the actions of other machines, and vehicles that can navigate through fields of interference on their own.
“There's no way that a human is going to be able to keep up with these new generations of cognitive electronic warfare systems that are constantly scanning the electromagnetic spectrum and jamming software where it can. Humans just won't be able to keep up with that. The expectation is once again, for electronic warfare, machines will fight against their machines.”
Electronic warfare is a data-rich field, as signals can be captured, recorded and studied in a way that most information on a battlefield cannot. That data, combined with machine learning that trains AI on how to interpret, understand, and counter those signals, makes cognitive electronic warfare an area where iteration on software is likely to yield outsized results
Having enough bandwidth available to allow pilots at the base to directly control drones flying in remote spaces is already something of a logistical triumph. As cognitive electronic warfare gets better, and the cost of putting that interference in place gets lower, directly piloting is going to be hard, especially from across the planet but even from closer bases.
Autonomy greatly reduces the amount of data an uncrewed vehicle needs to send back to the humans supervising it. As sensors get cheaper, collecting that information will be easier, but the bottleneck isn’t in the collection. It’s in the transmission.
“Getting the bandwidth to send it back will be an enormous challenge. No question about that. The communication environments of the future battlefield, will really be challenged by congestion and it will be challenged by active and probably effective interference by the adversary. So teleoperation is a challenge.”
Lower bandwidth in the field encourages autonomy, and autonomy in vehicles then means that the humans move from a dedicated pilot or sensor operator role into a sort of supervisory position, a commander of robots who can only reliable communicate in low-data messages.
Contested and denied electronic spectrum make areas once open to remote vehicles now hostile and possibly outright impossible. Designing machines that can get around those barriers, that can perform military tasks and missions even if they are out of contact from the humans that ordered them into action, is an adaptation to the environment.
It is a way to preserve the utility of uncrewed vehicles, without sending humans into that same danger. Or it a way to make sure that, when soldiers or marines find themselves trapped in a fight, rescue can still come in robotic form.
Autonomous machines are the tangible edge of what future war might look like. It’s the electromagnetic spectrum, invisible and always present and causing interference.
Pentagon imagines automation as seamless as in a strategy game
With a switch click, unknown tanks and infantry are clear, as our tank-commanding avatar holds a tablet with the adversary positions illuminated in red. Finding these adversaries are an array of systems, from satellites to drone swarms to uncrewed reconnaissance vehicles on the ground.
Another click, and the hostile forces on the screen are replaced by scorch marks, the tank commander’s tablet illuminated with the range of strikes called in from air and land forces.
While it exists in simulations and in games, perfect information on a battlefield remains an impossibility. Creating a “red force tracker;” that is, an intelligence collection process that provides real time information on where enemies are at all times, is a stretch for current technology. But it is one that could get closer to reality with autonomous robots scouting and providing information. This would take a great degree of information integration and distillation at the point of collection to work.
Rather than remote-control or teleoperated machines, future machines could be autonomous enough to require little human supervision, employ complex tactics, and to allow for a high degree of coordination with little need for communication.
“If we want to reduce load on soldiers, we have to get the equivalent of Siri for robots. We have to get the same interaction from a human-computer interface that a tank commander has with its driver, where it can maneuver in that space.”
Consider example of the tablet-commanded robot scouts and called-in strikes. This is a vision of military command where a human sits at the center of an autonomous body of sensors, perhaps gives them objectives but not specific targets, and then lets the machines process information to convert objects recorded with cameras into coordinates for where airplanes and artillery should place explosives. It’s a vision of war almost as seamless as a round of Command & Conquer real time strategies.
“The concept of the future Navy control room that we are working with is that it will not actually be on-board the ship. We think this will work because we know that you can have pilots anywhere controlling drones that fly over conflict space. So why not have the officers somewhere safe, instead of on-board?”
Control rooms, or operations rooms, on warships are where all of the information that is continuously collected by the vessel’s equipment, including radar, sonar and cameras, is relayed to captains so that they can make tactical decisions.
Unlike on-board control rooms where officers are usually seated, in case the ship is struck by a munition that would knock them off their feet, in an on-land control room officers could be allowed to walk freely around the room.
The AI in the process of development for the new control rooms could spot incoming threats to a warship and instantly prioritise them, so that commanders know which adversary vessels to take out first.
These technologies have the potential to transform warfare and greatly increase the situational awareness and efficiency of crews on board ships.”
Navy is seeing first-hand that data collection and analysis can go a long way in addressing lingering readiness problems, as the “Digital Twin” Office continues to roll out a set of pilot programs meant to introduce the service to the benefits of data science.
While its ultimate goal is to create a smarter data environment – much the way industry has used big data to better reach its customers, create efficiencies in production and more – the office’s first major action is to create awareness of its mission through a series of pilots that tackle readiness problems in the aviation and surface ship communities that have not been solved with traditional approaches.
The new organisation will assist systems commanders, type commanders, fleet commanders and others in identifying problems that can be solved using data analytics as a tool; facilitating a discussion between sailors and data scientists to create an approach for solving the problem; and then finding ways to apply that new approach to other parts of the Navy where applicable.
One pilot project looks at making surface ship maintenance availabilities more efficient, particularly as the Navy tries to embrace predictive maintenance, only fixing or replacing components as needed instead of on a fixed schedule.
Supply officers today focus on reactively replacing parts that are consumed from their inventory, instead of proactively predicting what parts will be needed and when, which would be more useful in a predictive maintenance environment.
“We want to look at one of our top surface readiness degraders and say, can we be more predictive of the supply side there, reduce the supply chain time and get better accuracy around how we provide parts for those types of repairs?”
“You might have to replace a blade on an engine, but you don’t necessarily order the bolts with that part. So can we create the relationships so that so whenever blade is replaced usually you have to have to also get this or that component – it’s kind of like e-shopping, if you bought this you may also want this – so can we create that kind of predictive supply system that would say, you just requested to replace the blade on the engine, you might also need these other parts because we find that there’s a high rate of these types of repairs with that as well.
And then it’s over to the user. The machine and the data have presented some courses of action; the user can say you don’t need that, you already checked, or you didn’t check that, maybe you need to do that, let’s go see. And hopefully then you can kind of compress these maintenance cycles a little more and speed up the time.”
Another pilot optimises operations of the ships’ power plants through a “Digital Twin” effort, where a virtual replica of the power plant is created so simulations can be run to understand how it will perform and require maintenance under various conditions.
“We wanted the communities to show that this wasn’t just some tech kind of application, but when you combine the digital and the physical expertise together, you could get outcomes that you otherwise couldn’t get if you tried to solve it on your own.
We wanted to expose the different aspects of the Navy to the tools and techniques, the language – a big part of this is, our users, our commanders have to be able to say, ‘We think there’s a data problem,’ instead of, ‘There’s a problem and let me go solve it in a traditional way.’
It’s a little bit different to say, ‘We think we have a data problem and we think we can get at this if only we had better access to data, or had the ability to analyze it differently, or had a different toolset.”
Office is addressing a major component of the Navy and Marine Corps readiness challenge: “non-mission capable, supply” aircraft that cannot fly because maintainers are awaiting the delivery of the spare parts needed to fix them. Both the maintainers and the supply community have separately tried to address this problem, which has only gotten worse in recent years due to insufficient funding. But the Office is coming at the problem from a new angle.
To begin the process, maintainers at the air station sat down with supply officers and data scientists to talk about the non-mission capable supply problem in a Digital Mission Space event.
“What we’ve seen at these Mission space centers is, in some cases the maintenance guys say, we can’t solve this problem, it’s really really hard; the supply guys are like, well we can help with that; and the data science guys are like, okay, well what data do you both have and how might we pull it together? And can we automate the process? Can we make it more predictive instead of reactive?
During the Digital mission space center event, the maintenance and the supply experts were asked to think about what data they collect, through what processes, and to what end. Data shouldn’t be collected for data’s sake and they were asked to consider how data collection is actually contributing to their mission.
The data scientists then helped them talk through what data could be shared and how, to assist each community in doing their jobs better, and to ultimately reduce the time it takes for an airplane to come into the depot, maintenance to begin, required parts to be identified, the right parts to arrive at the depot, and the airplane to be sent back out for operations.
Ultimately, the maintainers were given a new data set to work with and new processes to implement on a trial basis, and the mean time to repair aircraft is already down. The maintainers have also identified new policies, training areas and additions to their data environment that they think could benefit them going forward.
In addition to getting aircraft back to mission-capable status faster, the pilot is also sparking the right kind of conversation about using data to tackle problems, with maintainers considering how to pull data from other communities and from open-source documents such as historical weather data, as well as questioning what new data they may want to collect going forward.
Navy is starting to understand the power of data analysis to help address tough problems. It’s a bit unclear now how the office will spend its time and manpower after the first pilots wrap up, though it will likely include tackling more project ideas that come in and “We’re assisting commands in holding their own Digital Mission space events to brainstorm solutions to problems. We’re going to take lessons learned from these first pilot programs and using them to change the entire data environment the Navy has built for itself.
“We want to solve a problem, we want to show the community there’s value here so we can start thinking of, well we also have this other problem that might be something. And then we could start to pull that back and say, once we start to get that predictive supply piece, do we have the right data environment in order to make that scalable outside of surface readiness to other pieces?
“And then start walking the organisations through that and where they need to partner, and how to have the right contracts in place and the right acquisition strategy to go after those types of data environments. And do we have the right skillsets inside our workforce to be able to manage the data once we start collecting it and analyzing it?”
“Part of it is about the technology and the introduction of new technology, but the really big return, that ultra-boost you get, is out of redesigning your processes and streamlining them, making them simpler and making them focus on what the user really needs. And that’s where you get a big productivity kick.
When AI is introduced, new attack vectors are introduced, such as deep learning spoofing and data spoofing.
If the training data is known or manipulation of data is too predictable, adversaries can easily anticipate and predict actions and outcomes.
Adversaries can spoof sensors and also the data collected by those sensors without needing to mess with the underlying model code. Put simply, adversaries do not need to know what is in the box to exploit the box. Spoofing Process Models Efforts Results in Compromise of Sources/Methods:
1. Importance and necessity of AI transparency is application-specific.
2. Trust must be met across algorithms, data, and outcomes.
3. Users must understand the mechanisms by which systems can be spoofed.
4. Robust and resilient digital capability requires balancing development, operations, and security.
5. Network risk management security ownership throughout and across organisations is critical.
6. Applying AI requires a skilled and educated workforce with domain expertise, technical training, and the appropriate tools.
7. Organisations must develop workforce expertise in digital data models
8. Success for users in machine learning requires iteration, experimentation, and learning through early sub-optimal performance.
9. Organisation must build the foundational digital capability to successfully apply AI technologies-- database management, information integration.
10. Gaining competitive advantage through information and analytics is an enterprise-wide endeavor from headquarters to the deployed warfighter.