Stress Propagation in Human-Robot Teams Based on Computational Logic Model
This addresses stress management for small mission teams in high-stakes environments, but it is incremental as it adapts existing models like SIS to this specific context.
The paper tackled stress monitoring in human-robot teams to identify conditions leading to mission failure, resulting in a composite model combining computational logic, decision status, and stress propagation based on the SIS paradigm.
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.