Many real-world domains can benefit from adaptable decentralized task allocation through emergent specialization, especially in large teams of non-communicating agents. We begin with an existing bio-inspired response threshold reinforcement approach for decentralized task allocation and extend it to handle hierarchical task domains. We test the extension on self-deployment of a large team of non-communicating agents to patrolling a hierarchically defined set of areas. Results show near-ideal performance across all areas, while minimizing wasteful task switching through the development of specializations and subsequent respecializations when area demands change. A genetic algorithm is then used to evolve even more adaptable and stable task allocation behavior, by incorporating weight and power coefficients into agents’ response threshold reinforcement action probability calculations.

中文翻译：

---

分层域的适应性和稳定的分散任务分配

许多现实世界的领域可以通过紧急专业化从适应性分散的任务分配中受益，特别是在大型非通信代理团队中。我们从现有的用于分散任务分配的仿生响应阈值强化方法开始，并将其扩展到处理分层任务域。我们测试了一个大型非通信代理团队的自我部署扩展，以巡逻一组分层定义的区域。结果显示，所有领域的表现都接近理想，同时通过开发专业化和随后的再专业化，在领域需求发生变化时最大限度地减少浪费的任务切换。然后使用遗传算法进化出更具适应性和稳定性的任务分配行为，