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A KNOWLEDGE-BASED SYSTEM TO MODEL HUMAN SUPERVISORY CONTROL IN DYNAMIC PLANNING

机译:基于知识的动态规划中人监督控制系统

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With the increases in the levels of automation and computerization, supervisory control systems are becoming increasingly common in commercial and military applications. A supervisory control system consists of one or more human operators interacting with highly automated components such as those seen in satellite ground control, flexible manufacturing systems, or nuclear power plants. Humans typically perform cognitively intense tasks such as monitoring, planning, real-time control, and troubleshooting, and are ultimately responsible for the safe and efficient operation of the overall system. Although developments on supervisory control have led to useful applications in interface design and automation, there is a scarcity of research that empirically evaluates human decision making in supervisory control through emulation of task performance using knowledge-based systems. In the context of dynamic planning involving simulated search and rescue missions using ground based autonomous robots and uninhabited aerial vehicles, we developed a knowledge-based system that mimics supervisory control performance. This paper describes the application domain, the details of the simulation model, and the implementation and assessment of a knowledge-based system that mimics human supervisory control performance.
机译:随着自动化和计算机化水平的提高,监督控制系统在商业和军事应用中变得越来越普遍。监督控制系统由一个或多个人工操作员与高度自动化的组件(如在卫星地面控制,柔性制造系统或核电厂中看到的组件)交互作用组成。人们通常会执行诸如监视,计划,实时控制和故障排除之类的认知密集型任务,并最终负责整个系统的安全和高效运行。尽管监督控制的发展已导致在界面设计和自动化中的有用应用,但是仍然缺乏研究通过使用基于知识的系统模拟任务执行来凭经验评估监督控制中的人为决策的研究。在涉及使用地面自主机器人和无人驾驶飞行器进行模拟搜索和救援任务的动态规划的背景下,我们开发了一种基于知识的系统,该系统模仿了监督控制性能。本文介绍了应用领域,仿真模型的详细信息,以及模仿人类监督控制绩效的基于知识的系统的实现和评估。

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