A next generation firewall (NGFW) is a preemptive firewall that makes no distinction between the epistemic state of its learning and its objective role as an agent within a reinforcement learning paradigm.
Preemptive algorithms should be designed to prove the inequality of vulnerability probabilities of envariantly swappable Schmidt states. Geometric correlation is the firewall’s pedantic assumption; i.e., vulnerability probabilities are interchangeable when memory states are swapped.
The goal is to design preemptive algorithms using principles of envariance (among other things) for a NGFW to autonomously count the number of envariantly swappable vulnerabilities within any given system.
Thus, a NGFW is a multiply-stigmergic firewall being built by a novel configuration of artificial intelligence modules.
So, what's preemption exactly? Well, the essence of preemption is its ability to be represented by abstract geometric structures. Shapes, shapes of pieces, in some cases even the physical existence of shapes and its pieces, are all relevant for geometric arrangements of abstract structures. This includes the types of pieces, the number of pieces of each type, and the quantitative geometric power of pieces for abstract geometric structures.
Preemption has no inherent correspondence to the outside world. Therefore, computing preemption with abstract structures is free to correspond and interpret geometry, in any way that’s computationally viable.