We investigate the problem of dynamic sensor activation for decentralized decision making in partially observed discrete-event systems, where the system is monitored by a set of agents. The sensors of each agent can be turned on/off online dynamically according to a sensor activation policy. We define a general decentralized decision-making problem called the decentralized state disambiguation problem, which covers the decentralized control problem, the decentralized fault diagnosis problem, and the decentralized fault prognosis problem. The goal is to find a language-based minimal sensor activation policy for each agent such that the agents can always make a correct global decision as a team. A novel approach to solve this problem is proposed. We adopt a person-by-person approach to decompose this decentralized minimization problem into two centralized constrained minimization problems. Each centralized constrained minimization problem is then reduced to a fully centralized sensor activation problem that is solved effectively in the literature. The solution obtained is provably language-based minimal with respect to the system language.

中文翻译：

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分散式离散事件系统中传感器激活的最小化


我们研究了部分观察的离散事件系统中分散决策的动态传感器激活问题，其中系统由一组代理进行监控。每个代理的传感器可以根据传感器激活策略动态地在线打开/关闭。我们定义了一个通用的分散决策问题，称为分散状态消歧问题，它涵盖分散控制问题、分散故障诊断问题和分散故障预测问题。目标是为每个智能体找到基于语言的最小传感器激活策略，以便智能体作为一个团队始终能够做出正确的全局决策。提出了一种解决该问题的新方法。我们采用逐人的方法将这个分散的最小化问题分解为两个集中的约束最小化问题。然后，每个集中式约束最小化问题都被简化为完全集中式传感器激活问题，该问题在文献中得到了有效解决。所获得的解决方案可证明是相对于系统语言而言基于语言的最小解决方案。