The notion of goals in the context of real-world process-control is, more often than not, linked with a minimization of complex cost-functionals which are derived from mathematical postulates that depend upon the topography of hypersurfaces described by state-variables. However, in poorly-defined systems there are usually no plausible identification models available on which to apply such elegant techniques, other than notorious linearizations that are both approximate and ontologically unsatisfying. This paper shows how the goal of reducing oscillatory, transient behavior in any system can be achieved by augmenting a Proportional, Integral and Derivative (PID) schema with an AI paradigm that is in no way dependant on any model of the system. Results from a simulation of a second-order control system are included to show how the augmentation of this traditional PID controller by an AI-driven signature-table can significantly reduce transient oscillations in response to random step inputs and to thus achieve an important real- world goal. Commentary is made on how the seemingly negative factors of risk, uncertainty and failure are turned into positive contributors in the learning process.