1. An artificial neural network-based system for determining, for a specified knowledge domain in a given field of endeavor as represented in a neural network, a sought-for set of perturbations applicable to such knowledge domain that will produce a desired targeted result, comprising an artificial neural network that has an input layer, an output layer, and at least one hidden layer, and which is operable to produce outputs from said output layer when inputs are supplied to the input layer of said artificial neural network, said artificial neural network having been previously trained in accordance with training exemplars in a predefined field of endeavor to establish a particular knowledge domain therein, a network perturbation portion for iteratively perturbing said artificial neural network to effect iterative changes, subject to design constraints of the artificial neural network that remain unperturbed, in the outputs produced by said artificial neural network, the combined set of perturbations applied to said artificial neural network in any given iteration constituting a candidate set of perturbations, and a monitor portion associated with said artificial neural network to observe said data outputs produced by the artificial neural network in response to candidate sets of perturbations, said monitor portion including a comparator portion that operates to identify from among the observed data outputs being produced by the artificial neural network certain data patterns in said observed data outputs which satisfy predefined target criteria, identification of a data output that satisfies the predefined criteria determining a particular perturbation-output mapping relationship and establishing the candidate set of perturbations that has produced the desired targeted result as the sought-for set of perturbations.