This paper presents a potential-game approach for distributed cooperative selection of informative sensors, when the goal is to maximize the mutual information between the measurement variables and the quantities of interest. It is proved that a local utility function defined by the conditional mutual information of an agent conditioned on the other agents’ sensing decisions leads to a potential game, with the global potential being the original mutual information of the cooperative planning problem.

The joint strategy fictitious play method is then applied to obtain a distributed solution that provably converges to a pure strategy Nash equilibrium. Two illustrative numerical examples are presented to demonstrate good convergence and performance properties of the proposed game-theoretic method.

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