## 2 Background and Literature Review
### Popular HMT Simulation Methods  
The teaming concept of human and machine collaboration is an emerging field, and there is a substantial scope and necessity for simulation studies, owing to the intricacies involved in the variables of interest. There is not much information available in the literature that is directly related to simulation studies aimed at evaluation of human and machine team efficacy. Instead, simulation studies can be found on individual components of HMT in terms of interaction. Following are some of the prominent human-machine systems' software-based simulation methods. 1. Immersive learning simulation: This method is the integration of virtual, augmented, and mixed reality blended with a physical environment aimed at enhanced learning and training. The immersive simulation finds its application in classroom teaching, patient diagnosis, virtual gaming, drone flight control, and computer vision. Immersive learning primarily involves the user's attention and makes the virtual models indistinguishable from the real world. In this process, the user avails head mount displays or stereoscopic projection systems for simulations. With the human user as the subject of simulation, the user's inputs garnered in the process are evaluated as per the task-demands, relayed in conjunction with feedback [34]. 2. Modular open robots simulation engine (MORSE): This method is an open-source robotic simulator with a component-based architecture aimed at simulating sensors and actuators of robots [35]. The objective behind modular simulation is to evaluate various levels of the robotic component using the software-in-loop philosophy. The MORSE is capable of multiple simulations of heterogeneous robots (machines) in a fully 3D environment and provides a detailed assessment report. 3. Mixed-initiative system (MIS) plan model to test a team: In this method, the machine generates a plan of action for a specific task, and the user has the authority to intervene and modify the plan if necessary. Generally, learning from demonstration is a technique used in the literature, in which a human explicitly teaches a machine a specific skill. However, this does not fulfill a team spirit, which is based on the mutual adaption process for learning coherence in a joint action. In [36], the authors developed a teaming model of human factors engineering with the goal of achieving convergent team behavior during training and task execution. The team model is computationally encoded by extracting the knowledge of the shared mental model of human and machine. In the literature, human-in-the-loop simulations are also known as simulation-based training, and the common areas of application are air traffic control, emergency management, and military operations. For example, consider a UAV team with a single human team member that has the capability of redirection in-flight through transmission of GPS data. The key issue in human supervisory control of a UAV is how a dynamic system that requires constant human re-planning efforts could and should be designed [37]. 4. Air traffic control simulation: This method involves the exchange of air traffic management information between stakeholders. The dynamic variation in air traffic management procedures proves the necessity for careful evaluation of traffic control systems through human-in-the-loop simulations. Typically, the implications of potential changes in air traffic management information are initially evaluated through real-time software simulation with the help advanced probabilistic algorithms. This is followed by software-driven results that are used to further assess point-to-point human-in-the-loop connections. Due to the complexity involved in the traffic datasets, the chances of cognitive overload caused by stress factors on a human operator are significantly high, thereby adversely affecting the performance of the air traffic management system.