Mission teams are exposed to the emotional toll of life and death decisions. These are small groups of specially trained people supported by intelligent machines for dealing with stressful environments and scenarios. We developed a composite model for stress monitoring in such teams of human and autonomous machines. This modelling aims to identify the conditions that may contribute to mission failure. The proposed model is composed of three parts: 1) a computational logic part that statically describes the stress states of teammates; 2) a decision part that manifests the mission status at any time; 3) a stress propagation part based on standard Susceptible-Infected-Susceptible (SIS) paradigm. In contrast to the approaches such as agent-based, random-walk and game models, the proposed model combines various mechanisms to satisfy the conditions of stress propagation in small groups. Our core approach involves data structures such as decision tables and decision diagrams. These tools are adaptable to human-machine teaming as well.
翻译:这些小组由经过专门训练的人员组成,并配备有处理紧张环境和情景的智能机器。我们开发了一个综合模型,用于在这种人类和自主机器小组中进行压力监测。这种模型旨在确定可能导致任务失败的条件。拟议模型由三个部分组成:(1) 一个计算逻辑部分,静态地描述队友的压力状态;(2) 一个决定部分,随时显示特派团的状况;(3) 一个压力传播部分,以标准的可视化、可视化(SIS)模式为基础。与基于代理、随机行走和游戏模式等方法不同,拟议模型将各种机制结合起来,以满足小群体中的压力传播条件。我们的核心方法包括决策表和决策图等数据结构。这些工具也适用于人机械团队。