  This paper considers the problem of adaptively searching for an unknown
target using multiple agents connected through a time-varying network topology.
Agents are equipped with sensors capable of fast information processing, and we
propose a decentralized collaborative algorithm for controlling their search
given noisy observations. Specifically, we propose decentralized extensions of
the adaptive query-based search strategy that combines elements from the 20
questions approach and social learning. Under standard assumptions on the
time-varying network dynamics, we prove convergence to correct consensus on the
value of the parameter as the number of iterations go to infinity. The
convergence analysis takes a novel approach using martingale-based techniques
combined with spectral graph theory. Our results establish that stability and
consistency can be maintained even with one-way updating and randomized
pairwise averaging, thus providing a scalable low complexity method with
performance guarantees. We illustrate the effectiveness of our algorithm for
random network topologies.
