12:00, Wed 5 Nov 1997, WeH 7220 AutoRSM: Memory Based Active Learning for Noisy Optimization and Control Andrew Moore Joint work with Jeff Schneider, Justin Boyan, Scott Davies, Mary Soon Lee The Auton research project is an attempt to build an off-the-shelf learning software system for highly autonomous use in industrial control. A key component of Auton is AutoRSM (Autonomous Response Surface Methods) which is used for actively seeking data to build models and identify optimal operating regions. In this talk I will begin by frothing at the mouth about how important this problem is (and my obsession with it). I will discuss some of the classes of noisy optimization problems we need to attack. I will overview an array of possible approaches drawn from numerical analysis, statistics, controls, reinforcement learning, evolutionary computation and machine learning. I'll then discuss memory-based approaches to the problem and the potential merits. Our previous memory-based approaches have some drawbacks in terms of the difficulty (computational and statistical) of choosing the distance metric and size of the local region based on very limited data. I will introduce our current draft of a new algorithm, Q2, for robustly identifying regions of interest from limited experiments and I'll provide some highly preliminary initial results.