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 [1] in the setting of stochastic search, with application to target localization. We propose decentralized extensions of the active query-based search strategy that combines elements from the 20 questions approach of Tsiligkaridis et al. [2] and the social learning algorithm of Jadbabaie et al. [3]. We prove convergence to correct consensus on the true target location. This framework provides a flexible and tractable mathematical model for active decentralized target 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.