Wed 5 October, 12:00, WeH 1327 Response Surface Methods: An introduction, and preliminary discussion of the Auton project. Andrew W. Moore SCS and RI Response Surface Methods (RSMs) are a heavily used, statistical technique used in the optimization of (usually) expensive industrial processes. Such processes are typically characterized by: * A set of controllable parameters (which the Machine Learning community would call input variables). * A response function, which is a noisy measurement of the result of running a process with the given inputs. * We desire to find the set of parameters which optimizes the expected value of the response. * Experiments are very expensive, so in comparison computation is very cheap. This talk will introduce the basic techniques and statistics behind response surface methods. Current RSM practice is, for sensible reasons, a far from automatic process. Is it possible that techniques from Machine Learning and AI may be of use in RSM applications? They might permit us to design a new class of considerably more autonomous systems with which to robustly enhance, and in some cases even automate, current RSM practice. This talk will conclude with an outline of a new project to combine a number of machine learning tools, including memory-based learning and reinforcement learning, into such a system.