In this study, we investigate the effectiveness of using the PID (Proportional - Integral - Derivative) control loop factors for modifying response thresholds in a decentralized, non-communicating, threshold-based swarm. Each agent in our swarm has a set of four thresholds, each corresponding to a task the agent is capable of performing. The agent will act on a particular task if the stimulus is higher than its corresponding threshold. The ability to modify their thresholds allows the agents to specialize dynamically in response to task demands. Current approaches to dynamic thresholds typically use a learning and forgetting process to adjust thresholds. These methods are able to effectively specialize once, but can have difficulty re-specializing if the task demands change. Our approach, inspired by the PID control loop, alters the threshold values based on the current task demand value, the change in task demand, and the cumulative sum of previous task demands. We show that our PID-inspired method is scalable and outperforms fixed and current learning and forgetting response thresholds with non-changing, constant, and abrupt changes in task demand. This superior performance is due to the ability of our method to re-specialize repeatedly in response to changing task demands.

翻译：本研究探讨了利用PID（比例-积分-微分）控制回路因子来修正分散式、无通信型阈值群体中响应阈值的有效性。我们群体中的每个智能体拥有一组四个阈值，每个阈值对应其可执行的一项任务。当任务刺激高于对应阈值时，智能体将执行该特定任务。智能体能够动态地根据任务需求进行专业分工，这得益于其调整阈值的能力。当前动态阈值方法通常采用学习与遗忘过程来调整阈值，此类方法虽能有效实现单次专业分工，但在任务需求变化时难以重新实现专业分工。受PID控制回路启发，我们提出的方法基于当前任务需求值、任务需求变化量以及先前任务需求的累积和来调整阈值。实验表明，我们的方法具有可扩展性，并且在任务需求保持不变、持续变化或突变的情况下，其性能均优于固定阈值以及当前基于学习与遗忘的响应阈值方法。这种优越性能源于我们的方法能够针对不断变化的任务需求反复重新实现专业分工。