Abstract: A universal problem resolution method and system implementing continuous improvement for problem solving that utilizes simulative processing of relational data sets associated with initial states, allowed transition states, and goal states for a problem. The framework autonomously generates and solves higher order problems to find sequences of operations necessary to transform state sequences derived from the lower-order transformation simulations recursively. The solutions yield increasingly higher-order abstractions that converge to generalization such that the unwinding of the higher order sequences back down to the original problem yields the exact sequence of steps for unsolved instances of the problem in linear time without the need for re-simulation. Cooperating agents analyze solution path determinations for problems including those concerning their own optimization. This spawns state transition rules generalizable to higher layers of abstraction resulting in new knowledge enabling self-optimization.