Q2: memory-based active learning for optimizing noisy continuous functions

This paper overviews Q2, an algorithm for optimizing the expected output of a multi-input noisy continuous function. Q2 is designed to need only a few experiments, it avoids strong assumptions on the form of the function, and it is autonomous in that it requires little problem-specific tweaking. These capabilities are directly applicable to industrial processes, and may become increasingly valuable elsewhere as the machine learning field expands beyond prediction and function identification, and into embedded active learning subsystems in robots, vehicles and consumer products. Four existing approaches to this problem (response surface methods, numerical optimization, supervised learning, and evolutionary methods) all have inadequacies when the requirement of “black box” behavior is combined with the need for few experiments. Q2 uses instance-based determination of a convex region of interest for performing experiments. In conventional instance-based approaches to learning, a neighborhood was defined by proximity to a query point. In contrast, Q2 defines the neighborhood by a new geometric procedure that captures the size and shape of the zone of possible optimum locations. Q2 also optimizes weighted combinations of outputs, and finds inputs to produce target outputs. We compare Q2 with other optimizers of noisy functions on several problems, including a simulated noisy process with both nonlinear continuous dynamics and discrete-event queueing components. Results are encouraging in terms of both speed and autonomy