I would assume that in a military setting (as it is in most bureocratic/management settings) a solution like this has the immense advantage that one can precisely determine the source of any one error to a specific requirement/rule.
It is very hard to ask for management to trust a system they know nothing about, where they have literally no control over final behaviour, even if in the end it will perform better overall. In a rule-based system, instead, it is always possible to make adjustments and blame mistakes very efficiently to specific causes.
I guess this is the main reason why "true" AI is currently being used mostly in information fields, rather than on physical machines and engineering. No-one would know how to deal with the outcome of a fuzzy learned algorithm making the wrong decision. This is also a reason why autonomous cars are very interesting to me, even though I bet they are still full of ad-hoc rules in order to have a layer of "manageability" over the overall system.
I see how it can sound appealing to a bureaucrat, but as a programmer, debugging the concurrent evaluation of thousands of "natural" language IF...THEN... rules until I find the questionable one where a threshold was defined too low or too high sounds like a nightmare.
I imagine they would log all the inputs, as well as branches taken so they can later replay everything in a debugger. That would make the process much simpler.
Being able to take the blackbox recordings of your combat drone which got shot down, reconstruct the scenario and permute the rules till you get a win, seems like...well, a big win.
Air combat is also one of those areas which does notionally have narrowly computable victory parameters - given hardware of capabilities X, there is a model we don't know which should generally predict the outcome.
It is very hard to ask for management to trust a system they know nothing about, where they have literally no control over final behaviour, even if in the end it will perform better overall. In a rule-based system, instead, it is always possible to make adjustments and blame mistakes very efficiently to specific causes.
I guess this is the main reason why "true" AI is currently being used mostly in information fields, rather than on physical machines and engineering. No-one would know how to deal with the outcome of a fuzzy learned algorithm making the wrong decision. This is also a reason why autonomous cars are very interesting to me, even though I bet they are still full of ad-hoc rules in order to have a layer of "manageability" over the overall system.