Can LLMs make crowds more realistic?
Introducing Anisotropic Fields for Enhanced Diversity in Crowd Simulation
This research introduces Anisotropic Fields (AFs), a new method for building more realistic and complex crowd simulations by incorporating uncertainty into agent movement. Unlike traditional methods that rely on pre-defined rules and deterministic paths, AFs use probability distributions to guide agent behavior, resulting in diverse and emergent crowd movements.
For LLM-based multi-agent systems, this research is relevant because it offers a way to move beyond scripted interactions and create systems where agents exhibit more nuanced and unpredictable behavior. The ability to generate AFs from real-world data, like videos, also opens possibilities for training LLMs on complex, real-world behavioral patterns.