Distributed Probabilistic Bisection Search using Social Learning

TL;DR

This paper introduces a distributed probabilistic bisection algorithm leveraging social learning for target localization, which iteratively updates agents' beliefs through local queries and averaging, achieving faster convergence.

Contribution

The paper proposes a novel distributed algorithm combining Bayesian updates and social averaging, improving convergence rates in target localization tasks.

Findings

Outperforms existing methods in simulations

Provides theoretical bounds on convergence rate

Effective in noisy, distributed environments

Abstract

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.