A dynamic programming algorithm for finding an optimal sequence of informative measurements. (arXiv:2109.11808v3 [cs.LG] UPDATED)

An informative measurement is the most efficient way to gain information
about an unknown state. We give a first-principles derivation of a
general-purpose dynamic programming algorithm that returns an optimal sequence
of informative measurements by sequentially maximizing the entropy of possible
measurement outcomes. This algorithm can be used by an autonomous agent or
robot to decide where best to measure next, planning a path corresponding to an
optimal sequence of informative measurements. The algorithm is applicable to
states and controls …

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