from an input are used to influence the present or future behavior of the system. The feedback control model is governed by a set of equations that are used to predict the future state of the simuland and regulate its behavior. These equations – in conjunction with principles of control theory – are used to evolve physical operations of the simuland to include, but not limited to: dialogue, path planning, motion, monitoring, and lifting objects over time. Many times, these equations are modeled as nonlinear partial differential equations over a continuous time domain. Due to their complexity, powerful computers are necessary to implement these models. A consequence of using computers to simulate these models is that continuous systems cannot be fully calculated. Instead, numerical solutions, such as the Runge–Kutta methods, are utilized to approximate these continuous models. These equations are initialized from the response of one or more sources and rates of changes and outputs are calculated. These rates of changes predict the states of the simuland a short time in the future. The time increment for this prediction is called a time step. These new states are applied to the model to determine the new rates of changes and observational data. This behavior is continued until the desired number of iterations is completed. In the event a future state violates or comes within a tolerance of the violation the simuland will confer with its human counterpart seeking advice on how to proceed from that point. The outputs, or observational data, are used by the human operators to determine what they believe is the best course of action for the simuland. Their commands are fed with the input into the control system and assessed regarding its effectiveness in resolving the issues. If the human commands are determined to be valuable, the simuland will adjust its control input to what the human suggested. If the human's commands are determined to be unbeneficial, malicious, or non-existent, the model will seek its own correction approach. Domain and Codomain The domain for the models used to conduct adaptive collaborative control is commands, queries, and responses from the human operator at the finite-state machine level. Commands from the human operator allow the agent to be provided with additional input in its decision-making process. This information is particularly beneficial when the human is a subject matter expert or the human is aware of how to reach