(Perlin) Noise as AI coordinator

TL;DR

This paper introduces a novel framework using Perlin noise as a large-scale AI coordinator in games, achieving balanced, natural, and diverse agent behaviors across different environments.

Contribution

It adapts continuous Perlin noise signals for AI coordination, providing a reproducible framework that improves diversity, stability, and controllability of agent behaviors in large-scale game environments.

Findings

Perlin noise provides stable activation and spatial coverage.

The approach yields better diversity and regional balance.

It demonstrates competitive runtime performance.

Abstract

Large scale control of nonplayer agents is central to modern games, while production systems still struggle to balance several competing goals: locally smooth, natural behavior, and globally coordinated variety across space and time. Prior approaches rely on handcrafted rules or purely stochastic triggers, which either converge to mechanical synchrony or devolve into uncorrelated noise that is hard to tune. Continuous noise signals such as Perlin noise are well suited to this gap because they provide spatially and temporally coherent randomness, and they are already widely used for terrain, biomes, and other procedural assets. We adapt these signals for the first time to large scale AI control and present a general framework that treats continuous noise fields as an AI coordinator. The framework combines three layers of control: behavior parameterization for movement at the agent level, action time scheduling for when behaviors start and stop, and spawn or event type and feature generation for what appears and where. We instantiate the framework reproducibly and evaluate Perlin noise as a representative coordinator across multiple maps, scales, and seeds against random, filtered, deterministic, neighborhood constrained, and physics inspired baselines. Experiments show that coordinated noise fields provide stable activation statistics without lockstep, strong spatial coverage and regional balance, better diversity with controllable polarization, and competitive runtime. We hope this work motivates a broader exploration of coordinated noise in game AI as a practical path to combine efficiency, controllability, and quality.