On Decentralized Estimation with Active Queries

We consider the problem of decentralized 20 questions with noise for multiple players/agents under the minimum entropy criterion in the setting of stochastic search over a parameter space, with application to target localization. We propose decentralized extensions of the active query-based search strategy that combines elements from the 20 questions approach and social learning. We prove convergence to correct consensus on the value of the parameter. This framework provides a flexible and tractable mathematical model for active decentralized parameter estimation systems. We illustrate the effectiveness and robustness of the proposed decentralized collaborative 20 questions model for several different network topologies associated with information sharing.