Performance Prediction of Hub-Based Swarms

TL;DR

This paper introduces a graph-based modeling approach for hub-based swarms, enabling scalable prediction of collective behavior and performance in multi-agent systems.

Contribution

It presents a novel graph-based representation and encoding method that scales to many agents, facilitating analysis and prediction of swarm dynamics.

Findings

Low-dimensional embeddings effectively cluster collective states.

Structured trajectories can be learned and used to predict swarm performance.

The approach scales to larger colonies than previous models.

Abstract

A hub-based colony consists of multiple agents who share a common nest site called the hub. Agents perform tasks away from the hub like foraging for food or gathering information about future nest sites. Modeling hub-based colonies is challenging because the size of the collective state space grows rapidly as the number of agents grows. This paper presents a graph-based representation of the colony that can be combined with graph-based encoders to create low-dimensional representations of collective state that can scale to many agents for a best-of-N colony problem. We demonstrate how the information in the low-dimensional embedding can be used with two experiments. First, we show how the information in the tensor can be used to cluster collective states by the probability of choosing the best site for a very small problem. Second, we show how structured collective trajectories emerge when a graph encoder is used to learn the low-dimensional embedding, and these trajectories have information that can be used to predict swarm performance.