On Decentralized Estimation with Active Queries
It provides a mathematical model for decentralized active querying systems, but the results are incremental as they extend existing centralized methods to a multi-agent setting.
The paper proposes decentralized extensions of the 20 questions algorithm for multi-agent parameter estimation, proving convergence to correct consensus and demonstrating effectiveness on random network topologies.
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 stochastic 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 decentralized parameter estimation systems based on active querying. We illustrate the effectiveness and robustness of the proposed decentralized collaborative 20 questions algorithm for random network topologies with information sharing.