Actually, the practical experience of human vs AI pilot tended to indicate that humans are very predictable and not actually capable of grasping broad tactical picture.
The air force as of April 2025 still seems to disagree with this:
"Though AI quickly processes large datasets, it lacks context sensitivity and reasoning. In
HMT, human strengths (e.g. intuition and reasoning) and machines (e.g. fast data
processing) combine to maximize capability. For AI-enabled processes, the exercise of
appropriate human judgment is an important consideration, especially in strategically
high-risk operations."
https://www.doctrine.af.mil/Portals/61/documents/AFDN_25-1/AFDN 25-1 Artificial Intelligence.pdf
Trust but verify. Trust but verify.
There were DARPA tests, where during simulated air combat, a team of AI pilots on inferior aircrafts (4th gen fighters, wirh short and medium range missiles) soundly defeated a team of human pilots on superior aircrafts (5th gen fighters, with long-range missiles).
I read the paper you linked for this and it is absolutely a great read so thank you for that. And yes I think this is a really good simulation of the kinds of uncertainty and randomness faced during air combat. However, I would argue that the results obtained by this paper is what I would expect the general case to be. If the controlling AI can't even perform this, then CCAs are a failed gamble.
A few points of note:
- "The aircraft for both teams are identical in terms of their mechanical performance." then later on "computational cost and complexity of training an AI within AFSIM is a limiting factor"
- I think you may or may not have mis-remembered or maybe you were quoting some other study. All aircraft involved were the same save for loadout differences.
- This research group had done different experiments with different mission sets, but keep in mind that all involved aircraft are homogenous in capability and role. There are also limited actors in this. In the real ABMS, it'll be scaled up to include a much wider variety of capabilities and roles. As roles and capabilities increase, permutations between descritized rules also increases, which makes designing them even harder.
- "Each FIS has membership functions that classify the inputs and outputs into linguistic classifications, such as “far away” and “very threatening”, as well as if-then rules for every combination of inputs, such as “If missile launch computer confidence is moderate and mission kill shot accuracy is very high, fire missile”. By breaking up the problem into many sub-decisions, the solution space is significantly reduced. The cost of this approach is a risk of not accommodating for all sources of coupling. Through optimal design this can be minimized if not entirely
mitigated"
- As I said before, they turned an infinite solution space into a finite problem space by discretizing the problem space. They also assigned each fuzzy inference system to one task that takes a subset of rules, but that also means that you have to carefully design the rules coupling and discretization heuristics so that important cross effects between each FIS doesn't get missed. Remember that right now the scope is small and descritizing the problem space results in fairly small information loss. In something like ABMS, it's going to be a monumental job to try and design the heuristics that discretizes this space.
- Even as discretization makes the solution space scalable and more robust, you are still prone to problems like:
- "The fact that the intended opponent is a team of humans isan interesting complexity that is difficult to account for in the typical machine learning setting. The pilots could perform extremely strange, errant, and apparently suboptimal behaviors just to confuse or exploit ALPHA...To account for this and enable post-training testing andadjustments, a simple User Interface was created to allow humans to fly against ALPHA in-house. The human operators have been able to achieve a modest, but reasonable, amount of control over two blue aircraft to compete against ALPHA"
- These were humans flying against ALPHA initially with the control interface. In a near peer fight, it's going to be AI vs adversarial AI. This isn't insurmountable at all, but it does add further complexity and randomness that you either need suboptimal catch alls for, or huge resources devoted to learning or paring down the problem space.
- Also note that a UI was built to allow pilots modest control. This doesn't at all mean that this program couldn't test adversarial attempts to confuse and screw with the AI, but it limits the amount of crazy you could throw at it. Again - none of this is to say that this study is invalid or that human pilots flying against AI will necessarily always win, but the key point here is that an AI might excel at even complex tasks, but if you understand the system and its constraints, there may be situations where you can take measures that the AI doesn't understand or know about. And how much the AI can counteract this depends on how good your adversarial training is.
- Hand in hand with the previous point is that it takes time for an AI to be trained to adapt to new tactics and weapons.
- As I said before, whether its this study or DARPA's ACE program, we are addressing the 90% here and we try our best to address the remaining 10% too, but as with manufacturing, you always need to account for the fact that you can't account for everything. So at a certain point it becomes a tradeoff for developing a solution to a problem vs accepting partial or total loss of the platform. More often than not, we develop for the 90%, reach for the 10%, achieve 94% and leave the remaining 6% for the human to decide on.
- Geno noted how the first generation of red ALPHA held its own against the blue variant of ALPHA, but the resulting engagements often ended with heavy losses for both sides. Psibernetix and Geno worked together to develop tactics, techniques, and procedures to overcome red ALPHA’s payload and no-AWACS disadvantage, capitalize on blue’s mistakes, and take advantage of numeric platform superiority (when the situation presented itself).
- This expresses what my point here, which is that there remains many cases when a human is needed to enrich how the AI learns. With new tactics and capabilities introduced, the more disruptive they are, the more you need the human in the loop to provide said context.
The AI won all engagements through better coordination and the ability to plan fast 3D actions in real time.
Yes indeed. Humans cannot compare in reaction time and the number of variables we can adequately consider at any given moment.
Basically while human pilots were only able to concentrate on immediate goals and threats, and acted more or less individually (and withing predictable tactical schemes), AI planned in advance, so each its action was part of a global tactical scheme.
You'd conclude that if you strip away the context in which this observation was made - which was in a bounded and limited simulation in the context of this report. It's a long stretch to say that humans acted individually and didn't do long term planning without actually understanding in greater detail how the control of the human controlled blue team was set up. Note that blue was described as taking overly agressive shots and maneuvers while ALPHA controlled red was conservative and precise about its timetables. It's not terribly hard to understand
why blue was aggressive given the loadout advantage.
- The test never flipped the loadout and situational advantages. Although I'm sure ALPHA may well win that too, there's no telling whether it makes similar decisions to human pilots i.e high aggression and shooting first
- ALPHA was trained by EVE on the baseline controller AFRL uses, which means it's trained specifically to fight with and against the predictable and common doctrine rules. To what degree the interface allows one to stray from this doctrine and to what degree ALPHA can counter these isn't clear.
Having said that, yes - humans usually don't make make optimal decisions, but it's not necessarily because AI plans ahead in ways that humans can't or can't be assisted to achieve, it's the decision loop and considered parameters that outpaces humans.
What this doesn't account for or doesn't account for completely are the rest of the 10%. The paper does talk extensively about using real world harvested data that accounts for some level of system associated failures, but that's only the tip of the iceberg here. In practice, you have immediate jamming, deceptive EW, lossy links, and delays and connection time delays. You have elements that may or may not be currently connected to the system. You have manned elements already performing non-optimally compared to what AI would do. You also have to remember that large models with a ton of parameters do begin to do poorly unless you can structure that data using well designed algorithms. You also can't fully trust AI to train itself to discover these algorithms either because in most instances you must be able to interpret
why the AI decided to do what it does to verify that it isn't misleading itself. So ultimately, at best, AI is at the stage where at the very minimum a human is needed to trust and verify critical decisions. It's why there's a whole sub-field of ML dedicated to interpretable machine learning.
Another thing of note here is that ALPHA is a central node controller that omnipotently understands the state of each commanded aircraft. Realistically, you aren't going to have that in the real world. In the real world ABMS or the BMS system in controlling fighters is going to be taking on the high level controls of the flight while the state of each part of the system requires transmission in order to be communicated. The federated nature of this now introduces another factor, which is that your central BMS that acts so well coordinated in the simulation now relies on those transmission links in order to make the critical decisions - links that can be jammed, degraded, fooled, lossy, high latency or lost entirely due to various hardware / software problems.
As I said above; the AI is able to comprehend the whole situation in real time, and plan even defensive reactions in such way, that its forces would gain advantage from such defensive reactions. It's like a real-time chess play in 3D. The AI main advantage against humans is not the G-force constrains, but the ability to evaluate situation faster and much more comprehensively, and plan accordingly.
Again - that "whole situation" is a situation reduced in scope and simplified greatly by having homogenous participants, limited numbers and an unclear control mechanisms given to the human in controller. It's feedback loop is faster and it can account for more variables. It can probably be built to ameliorate many of the difficulties I've listed already, but when it encounters things that forces it into catch alls, those catch alls may or may not be optimal anymore.
To conclude, while this is all very promising, when you actually account for implementation, limitations and encountered operational difficulties, the reality is far away from the simplistic and over generalized conclusions being drawn.