  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.
