  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 numerical examples on
simplified weather forecasting and range-only target tracking verify
convergence and performance characteristics of the proposed game-theoretic
approach.
