Asynchronous Decentralized 20 Questions for Adaptive Search

TL;DR

This paper introduces a decentralized adaptive search algorithm for multiple agents using a 20 questions approach, ensuring convergence and robustness in dynamic network topologies with noisy observations.

Contribution

It develops a novel decentralized extension of the 20 questions search strategy that guarantees convergence under time-varying networks with noisy data.

Findings

Proves convergence to correct consensus in dynamic networks.

Demonstrates robustness with one-way updates and randomized averaging.

Validates effectiveness through simulations on random network topologies.

Abstract

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.