Murciano A, Millán J R, Zamora J
Departamento de Biomatemática, Universidad Complutense de Madrid, Spain.
Biol Cybern. 1997 May;76(5):375-82. doi: 10.1007/s004220050351.
Specialization is a common feature in animal societies that leads to an improvement in the fitness of the team members and to an increase in the resources obtained by the team. In this paper we propose a simple reinforcement learning approach to specialization in an artificial multi-agent system. The system is composed of homogeneous and non-communicating agents. Because there is no communication, the number of agents in the team can easily scale up. Agents have the same initial functionalities, but they learn to specialize and so cooperate to achieve a complex gathering task efficiently. Simulation experiments show how the multi-agent system specializes appropriately so as to reach optimal (or near-to-optimal) performance in unknown and changing environments.
专业化是动物群体中的一个常见特征，它能提高团队成员的适应性，并增加团队获取的资源。在本文中，我们提出了一种简单的强化学习方法，用于人工多智能体系统中的专业化。该系统由同质且不进行通信的智能体组成。由于不存在通信，团队中的智能体数量可以轻松扩展。智能体具有相同的初始功能，但它们通过学习实现专业化，从而合作高效地完成复杂的聚集任务。仿真实验展示了多智能体系统如何在未知和变化的环境中适当地实现专业化，以达到最优（或接近最优）性能。
