In this paper we investigate the problem of controlling a partially observed dynamical system such that its state is difficult to infer using a(fixed-interval) Bayesian smoother . This problem arises naturally in applications in which it is desirable to keep the entire state trajectory of asystem concealed . We show that the entropy of Bayesian smooths can be expressed as the sum of entropies of marginal state estimates given by Bayesian filters . This novel additive form allows us to reformulate the smoothing-averse control problem as a fullyobserved stochastic optimal control problem in terms of the usual concept of the information (or belief) state, and solve the resulting problem via dynamicprogramming . We illustrate the applicability of smoothing toprivacy in cloud-based control

Author(s) : Timothy L. Molloy, Girish N. Nair

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Keywords : problem - control - state - smoothing - bayesian -

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