Distributed Probabilistic Bisection Search using Social Learning
This addresses target localization for distributed networks, but it appears incremental as it builds on existing probabilistic bisection and social learning techniques.
The paper tackles the problem of target localization by proposing a distributed probabilistic bisection algorithm using social learning, where agents perform Bayesian updates and average beliefs over local networks, and it shows that the method outperforms current state-of-the-art methods in numerical simulations.
We present a novel distributed probabilistic bisection algorithm using social learning with application to target localization. Each agent in the network first constructs a query about the target based on its local information and obtains a noisy response. Agents then perform a Bayesian update of their beliefs followed by an averaging of the log beliefs over local neighborhoods. This two stage algorithm consisting of repeated querying and averaging runs until convergence. We derive bounds on the rate of convergence of the beliefs at the correct target location. Numerical simulations show that our method outperforms current state of the art methods.