Response threshold reinforcement is a powerful model for decentralized task allocation and specialization in multiagent swarms. In dynamic environments, initial task assignments and specializations must be updated over time to meet changing system needs. The very nature of threshold reinforcement-based behavior can, however, hinder respecialization, limiting its usability in real-world applications. We propose a decentralized forgetting-based extension to response threshold reinforcement and show that it can improve the efficiency and stability of the resulting task assignments under changing system demands.

中文翻译：

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通过忘记增强的阈值来使群体专业化

响应阈值增强是用于多任务群中分散任务分配和专业化的强大模型。在动态环境中，初始任务分配和专业化必须随时间更新，以满足不断变化的系统需求。但是，基于阈值增强的行为的本质可能会阻碍重新专业化，从而限制了其在实际应用中的可用性。我们提出了基于分散的基于遗忘的响应阈值扩展，并表明它可以在不断变化的系统需求下提高结果任务分配的效率和稳定性。