Pid-Inspired Modifications in Response Threshold Models In Swarm Intelligent Systems
Pid-Inspired Modifications in Response Threshold Models In Swarm Intelligent Systems
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DOI：
10.1145/3583131.3590442
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发表时间：
2023-04
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影响因子：
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通讯作者：
Maryam Kebari;A. Wu;David Mathias
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文献类型：
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作者：
Maryam Kebari;A. Wu;David Mathias
在这项研究中，我们研究了使用PID（比例-积分-微分）控制回路因子在分散的，非通信的，基于阈值的群中修改响应阈值的有效性。我们群中的每个代理都有一组四个阈值，每个阈值对应于代理能够执行的任务。如果刺激高于其相应的阈值，代理将对特定任务采取行动。修改其阈值的能力允许代理动态地专门化以响应任务需求。动态阈值的当前方法通常使用学习和遗忘过程来调整阈值。这些方法能够有效地专门化一次，但如果任务需求发生变化，则很难重新专门化。我们的方法受到PID控制回路的启发，根据当前任务需求值、任务需求的变化以及先前任务需求的累积和来改变阈值。我们表明，我们的PID启发的方法是可扩展的，优于固定和当前的学习和遗忘响应阈值不变，恒定和突然变化的任务需求。这种上级性能是由于我们的方法能够重复重新专业化以响应不断变化的任务需求。
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.