Machine Learning and Robust Automatic Control of Complex Systems with Stochastic Factors

Given a set of input data and one or more performance metrics, this method searches directly for a region of specified size, said size representing a selected amount of random variation of the data that provides a preferred, but not necessarily optimal, value of the performance metric across the region. Repeated executions of this method over time yield a good, but not necessarily provably optimal, path through unstable conditions, as for a vessel or aircraft seeking a relatively quick path through changing turbulence. Using repeated executions to derive paths also supports selection of smooth automatic control, over time, of a system subject to random variations in conditions, this method greatly reduces sharp changes in control parameters as conditions change, while selecting good sets of control parameters at each re-computation.