MAX-consensus in open multi-agent systems with gossip interactions

TL;DR

This paper addresses the challenge of computing the maximum value in open multi-agent systems where agents can join or leave, proposing algorithms that adapt to changing agent populations through gossip interactions.

Contribution

It introduces algorithms for maximum computation in open multi-agent systems with dynamic agents, handling agent departures and arrivals during execution.

Findings

Algorithms successfully compute the maximum despite agent churn.

Different mechanisms are proposed for agents leaving with or without last messages.

The methods ensure eventual convergence to the correct maximum value.

Abstract

We study the problem of distributed maximum computation in an open multi-agent system, where agents can leave and arrive during the execution of the algorithm. The main challenge comes from the possibility that the agent holding the largest value leaves the system, which changes the value to be computed. The algorithms must as a result be endowed with mechanisms allowing to forget outdated information. The focus is on systems in which interactions are pairwise gossips between randomly selected agents. We consider situations where leaving agents can send a last message, and situations where they cannot. For both cases, we provide algorithms able to eventually compute the maximum of the values held by agents.