Distributed Probabilistic Bisection Search using Social Learning

2016/08/21 by Athanasios Tsiligkaridis, Theodoros Tsiligkaridis, Tsiligkaridis, Athanasios +1
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Social and Information Networks (cs.SI) #cs.LG #cs.MA #cs.SI

paper · pdf · doi:10.48550/arxiv.1608.06007

5 Pages, Accepted to ICASSP 2017

arxiv created 2016/12/28 · arxiv updated 2016/12/30

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.

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