The Future of Air Warfare: Evolutionary Symbiotic Enhancement with a Virtual Second-Seater

The recent DARPA AlphaDogfight Trials (ADT) were an impressive display of both technology and competition in support of advancing American airpower. As part of a broader DARPA technology and experimentation effort called Air Combat Evolution (ACE), in just over a year, the ADT has pushed the state-of-the-art for the use of agent-based modeling and artificial intelligence () applications to air warfare. Much of the initial reporting and commentary about ADT focused on the unambiguous final result when AI defeated the human pilot in each of their five dogfights. Here, as in the past, when such a decisive result occurs, some herald it as the end of an era and the dawn of a new one, like the shift from cavalry to tanks. Conversely, skeptics highlight the unrealistic conditions that applied to the test, such as the fact that the ADT used “perfect” data during the scenario conditions, a fact that any experienced pilot or controller would identify as unrealistic. In the ADT, this meant that a kill was adjudicated by reaction time in close quarters, which gives a significant inherent advantage to the AI These artificialities aside, DARPA appropriately chose a technically challenging but simplified tactical problem for this cutting-edge experimentation in air warfare. What then should we learn from the experiment?

High-profile DARPA experiments, like the ADT, are critical catalysts to stimulating technology and industrial ecosystems, while also pushing the boundaries of the state-of-the-art, embracing competition and learning, and inspiring the wider technology community. DARPA’s use of this approach has included the Grand Challenge series in the mid-2000s, which helped to spawn the self-driving car industry and multiple associated technologies, and the mid-2010s Cyber Grand Challenge, which pushed the state-of-the-art for detecting, patching and exploiting software vulnerabilities - healing friendly systems while also attacking adversary systems. The U.S. Services would benefit from more consistently embracing this type of approach to promote innovation and progress, along with more acquisition programmatic “on-ramps,” so mature technologies can be included in critical weapon system upgrades.

Ideas of individual combat loom large in the military aviation community’s ethos of “aerial knights” dueling in the sky, using quick reaction maneuvers in close proximity to win. However, 1 v. 1 aerial gun-based dogfighting, or even short-range missiles, are increasingly a relic of air-to-air combat from the age before sensors and missiles grew in range, sophistication and lethality. For several decades now, technology advancements have already enabled air warfare to evolve from dogfighting to beyond visual range missile engagements. Future air warfare scenarios are unlikely to include 1 v. 1 dogfighting between aircraft using guns, due to advances in sensor ranges and fusion, along with network-enabled weapons and cooperative teaming, that are already fielded on 5th generation fighters. Weapons and deployable platforms using increasingly sophisticated combinations of these technologies will be able to more easily kill a target or team together to achieve more advantageous positioning for a successful kill.  

Further efforts to incorporate uncertainty, such as fuzzy logic controllers, will make simulated combat conditions and performance with AI more realistic and enable effective transition to real-world conditions. Although excited speculation continues about how AI will replace a pilot in the cockpit and thus enable an unmanned fighter aircraft to pull many more g's than a human, this advantage already exists today: a human deployed missile can pull many more g’s than a fighter aircraft. Such considerations aside, the types of AI approaches that ADT demonstrated are also a valuable way for DoD to define for industry how to further enhance a missile’s ability to dogfight with a target. Using AI’s strength to assess aircraft maneuvers and transitions will allow pilots to have a higher probability of kill from a wider range of conditions, including at the boundaries of a weapons engagement zone. AI's maturation has been, and will likely continue to be, much more evolutionary than revolutionary. 

As aviation technology advanced, complex mechanical systems were replaced by analog switchboards in the cockpit, requiring an aircraft second-seater. Subsequently, digitized cockpits with sensor readouts, autopilot, and automated navigation followed. While current systems even include automated take-off and landing, integrated displays to aid with mission planning and weapons selection, as well as coordination across flight groups. Correspondingly, the preponderance of pilot workload for advanced aircraft continues to shift from being primarily about how to best fly the plane, instead of using the aircraft’s own sensors and weapons, in conjunction with other offboard assets most effectively in support of the Joint Force. This crucially and fundamentally shifts pilots' emphasis from manning the equipment (the fighter) to equipping the pilot to perform a wider range of functions in more lethal and effective ways.

Dr. Tim Grayson, Director of DARPA’s Strategic Technology Office, recently emphasized a critical perspective to understanding ADT: not as a contest between either human or machine, but instead heralding an era of “human-machine symbiosis.” Applying this concept more generally to air warfare, key examples can be thought of as similar to the five types of symbiosis: a Flight Group, with either all human pilots or all autonomous drones, such as in a swarm (mutualism); deployment of attritable payloads or platforms, while the human pilot remains as a rear area local controller (commensalism); offensive or defensive attritables or expendables with signature abilities (mimicry); deployment of expendable payloads (e.g., from an “arsenal plane”) in salvos of homogenous or heterogenous weapon mixes (amensalism); and deployment of cyber effects into a threat payload and/or platform, for immediate or latent impacts (parasitism).

AI is the critical enabler in each of these manifestations of human-machine symbiosis for air warfare. Further advancements in human-machine symbiosis will enable the pilot to focus more on larger area mission planning while delegating the business of flying to the AI No longer will the pilot selection process need to emphasize the quickest reaction times and physiology for high g forces, but should instead prioritize aptitudes for synthesizing information, tasking prioritization and decision-making. These skills will be critical to leverage and coordinate effects from a pilot’s own aircraft, as well as other offboard assets. Abstraction layers will result in less onerous and specialized training required to fly a plane, and more emphasis and ease on how to best use it.

The ultimate goal of using AI in warfare is to provide decisive advantage in an engagement to achieve victory. The ADT and broader DARPA ACE program is a crucial catalyst to spawn advancements for “pilot assist” technologies, just like driver-assist technologies continue to lay a crucial foundation for future driverless cars. Today, AI can become the virtual “second seater,” able to navigate and perform complex flying functions, while the human pilot retains more of a Weapon Systems Officer (WSO) focused role. The AI virtual second seater function will enable algorithms to learn from the human operator, further building trust, sophistication, and capability in the near term. To further build trust with human pilots, ensure algorithm explainability, and enhance AI learning and capability, the U.S. Services should establish a version of mission debrief for AI in both virtual and real-world employment.

The use of AI-based pilot assist technologies will enable a human pilot’s role to shift to a weapons version of air traffic control: a combination of local air battle manager and platoon leader in the sky. As a local aerial mission commander, the human pilot’s focus can be on directing a pack of other assets– either as platforms, or network-enabled weapons with smart control, such as DARPA’s Gremlins or the USAF’s Golden Horde programs. As a local air warfare conductor, the pilot can be close to the action, but from a safe vantage point, allowing for unmanned platforms and weapons to synchronize and improvise. This will necessitate a radical shift in pilot training to focus on cultivating mission command at a higher echelon level, but at a much earlier stage in a human pilot’s career. Organizationally, this will also require a reformulation of the concept of the U.S. Air Force “Flights” echelon to one in which a human oversees and directs each Flight but delegates AI control over each of the corresponding supporting "Elements." This approach parallels and can leverage ongoing developments in other sectors, such as industrial robotic manufacturing, autonomous shipping flotillas, and autonomous trucking and taxi fleets. In each of these cases, automation and AI enable lower-level tasking to be performed, while abstraction enables a human to oversee the coordination and conduct of multiple assets in a feasible and constructive way.

The U.S. Services continue to have revolutionary visions of blended flight groups of manned fighters and loyal wingmen, or fully autonomous multi-role fighters. The challenges to achieving these visions are not insurmountable, but fully realizing them will not be as quickly as desired. However, a significant evolution of air warfare is already underway. Over the last decade, the rapid convergence of key technologies is occurring beyond AI and machine learning. These technologies include fusion engines, miniaturized active electronically scanned array (AESA), onboard processing, advanced displays, and interfaces, swarming coordination, sophisticated multivariate recommendations, palletized munitions, controllers for many vs. many, abstraction layers, and systems to enable conversion of legacy platforms to unmanned (e.g., the F-16 to a QF-16). The combination of these technologies will soon allow a pilot to lead a force of previously obsolete converted unmanned aircraft into battle, loaded with network-enabled weapons, and re-enforced by palletized munitions deployed from rearward air bastions of relative safety. Select unmanned squadrons, or swarms of munitions, will be able to engage in specific deconflicted sectors of operation while operating at appropriate levels of balanced trust and risk. In parallel to these technologies, a more pragmatic conceptualization is required for how AI can be best used. A.I is not a pilot replacement but can be a virtual second-seater, enabling rapid and continuous evolution of human-machine symbiosis for decisive advantage. Instead of considering options for either human pilots or AI-based replacements, DARPA and the U.S. Services, through a symbiosis of commercial and military partnership, should focus on advancing virtual second-seaters for air warfare victories into the next decade and beyond.

Christopher C. Bassler is a Senior Fellow at the Center for Strategic and Budgetary Assessments.

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