Neural Network Exploration Using Optimal Experiment Design

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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David Cohn


Consider the problem of learning input/output mappings through exploration, e.g. learning the kinematics or dynamics of a robotic manipulator. If actions are expensive and computation is cheap, then we should explore by selecting a trajectory through the in(cid:173) put space which gives us the most amount of information in the fewest number of steps. I discuss how results from the field of opti(cid:173) mal experiment design may be used to guide such exploration, and demonstrate its use on a simple kinematics problem.