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