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[2602.18947] (Perlin) Noise as AI coordinator
Summary
The paper explores using Perlin noise as a coordinator for AI in large-scale game environments, addressing challenges in balancing behavior and coordination among non-player agents.
Why It Matters
This research is significant as it presents a novel approach to AI coordination in gaming, potentially enhancing the realism and efficiency of non-player character behavior. By leveraging Perlin noise, the study aims to improve the diversity and control of agent actions, which could have broader implications for AI applications beyond gaming.
Key Takeaways
- Perlin noise can effectively coordinate AI behavior in gaming environments.
- The proposed framework combines behavior parameterization, action scheduling, and event generation.
- Experiments demonstrate improved stability, diversity, and efficiency in agent coordination.
Computer Science > Artificial Intelligence arXiv:2602.18947 (cs) [Submitted on 21 Feb 2026] Title:(Perlin) Noise as AI coordinator Authors:Kaijie Xu, Clark Verbrugge View a PDF of the paper titled (Perlin) Noise as AI coordinator, by Kaijie Xu and 1 other authors View PDF HTML (experimental) 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, filter...