Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behavior was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or simple action rules to shape the decision of each agent and the collective behavior. However, manual tuned decision rules may limit the behavior of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any explicit rule. We evolve a swarm of agents representing an ant colony. We use an evolutionary algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behavior of each agent. The goal of the evolved colony is to find optimal ways to forage for food and return it to the nest in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide other ants. The pheromone usage is not manually encoded into the network; instead, this behavior is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication via pheromone did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can efficiently complete the foraging task in a short amount of time. Our approach illustrates self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.

翻译：社会性昆虫（如蚂蚁）通过信息素进行交流，从而能够协调群体活动并解决复杂任务（例如觅食）。这种行为是在进化过程中塑造形成的。在计算模型中，群体自协调通常通过概率性或简单行动规则来实现个体决策与集体行为。然而，人工调参的决策规则可能限制群体的行为表现。本研究探索无显式规则条件下进化群体中自协调与交流行为的涌现机制。我们构建模拟蚁群的智能体群体，采用进化算法优化脉冲神经网络（SNN）作为人工大脑控制器，驱动每个智能体的行为。进化蚁群的目标是在最短时间内找到最优觅食路径并将食物运回巢穴。在进化阶段，蚂蚁通过在学习在食物堆和巢穴附近释放信息素引导同伴，形成协作行为。信息素使用机制并非人工编码进网络，而是通过优化过程自发建立。实验表明，与未涌现信息素交流的蚁群相比，基于信息素通信的群体具有更优表现。我们将SNN模型与规则驱动系统进行觅食性能比较，结果显示SNN模型能在短时间内高效完成觅食任务。本方法论证了网络优化过程中信息素介导的自协调机制涌现的可能性。该研究为利用SNN作为底层架构实现多智能体交互（需具备交流与自协调能力）的复杂应用提供了概念验证。