We introduce a simulation environment to facilitate research into emergent collective behaviour, with a focus on replicating the dynamics of ant colonies. By leveraging real-world data, the environment simulates a target ant trail that a controllable agent must learn to replicate, using sensory data observed by the target ant. This work aims to contribute to the neuroevolution of models for collective behaviour, focusing on evolving neural architectures that encode domain-specific behaviours in the network topology. By evolving models that can be modified and studied in a controlled environment, we can uncover the necessary conditions required for collective behaviours to emerge. We hope this environment will be useful to those studying the role of interactions in emergent behaviour within collective systems.

翻译：我们引入了一个仿真环境，以促进对涌现集体行为的研究，重点在于复现蚁群的动力学特性。该环境利用真实世界数据，模拟出一条目标蚁群路径，要求可控智能体通过学习来复现该路径，学习过程基于目标蚂蚁观测到的感官数据。本研究旨在为集体行为模型的神经进化做出贡献，重点关注在神经网络拓扑结构中编码领域特定行为的神经架构进化。通过在受控环境中进化可修改和研究的模型，我们能够揭示集体行为涌现所需的必要条件。我们希望该环境能够为研究集体系统中相互作用在涌现行为中的角色提供支持。