Too Global To Be Local: Swarm Consensus in Adversarial Settings

TL;DR

This paper investigates how a swarm of robots can reach consensus despite adversarial members, modeling the problem as a two-player game and analyzing optimal strategies through simulations.

Contribution

It introduces a game-theoretic framework for swarm consensus under adversarial conditions and compares optimal strategies in simplified models.

Findings

Globally optimal strategies outperform local strategies in simulations.

The optimal solution is unattainable in fully distributed settings.

A performance metric correlates with winning chances in the game.

Abstract

Reaching a consensus in a swarm of robots is one of the fundamental problems in swarm robotics, examining the possibility of reaching an agreement within the swarm members. The recently-introduced contamination problem offers a new perspective of the problem, in which swarm members should reach a consensus in spite of the existence of adversarial members that intentionally act to divert the swarm members towards a different consensus. In this paper, we search for a consensus-reaching algorithm under the contamination problem setting by taking a top-down approach: We transform the problem to a centralized two-player game in which each player controls the behavior of a subset of the swarm, trying to force the entire swarm to converge to an agreement on its own value. We define a performance metric for each players performance, proving a correlation between this metric and the chances of the player to win the game. We then present the globally optimal solution to the game and prove that unfortunately it is unattainable in a distributed setting, due to the challenging characteristics of the swarm members. We therefore examine the problem on a simplified swarm model, and compare the performance of the globally optimal strategy with locally optimal strategies, demonstrating its superiority in rigorous simulation experiments.