Triggering Adaptive Automation in Naval Command and Control

In many control domains (plant control, air traffic control, military command and control) humans are assisted by computer systems during their assessment of the situation and their subsequent decision making. As computer power increases and novel algorithms are being developed, machines move slowly towards capabilities similar to humans, leading in turn to an increased level of control being delegated to them. This technological push has led to innovative but at the same time complex systems enabling humans to work more efficiently and/or effectively. However, in these complex and information-rich environments, task demands can still exceed the cognitive resources of humans, leading to a state of overload due to fluctuations in tasks and the environment. Such a state is characterized by excessive demands on human cognitive capabilities resulting in lowered efficiency, effectiveness, and/or satisfaction. More specifically, we focus on the human-machine adaptive process that attempts to cope with varying task and environmental demands. In the research field of adaptive control an adaptive controller is a controller with adjustable parameters and a mechanism for adjusting the parameters (Astrom & Wittenmark, 1994, p. 1) as the parameters of the system being controlled are slowly time-varying or uncertain. The classic example concerns an airplane where the mass decreases slowly during flight as fuel is being consumed. More specifically, the controller being adjusted is the process that regulates the fuel intake resulting in thrust as output. The parameters of this process are adjusted as the airplane mass decreases resulting in less fuel being injected to yield the same speed. In a similar fashion a human-machine ensemble can be considered an adaptive controller. In this case, human cognition is a slowly time-varying parameter, the adjustable parameters are the task sets that can be varied between human and machine, and the control mechanism is an algorithm that ‘has insight’ in the workload of the human operator (i.e., an algoritm that monitors human workload). Human performance is reasonably optimal when the human has a workload that falls within certain margins; severe performance reductions result from a workload that is either too high or (maybe surprisingly) too low. Consider a situation where the human-machine ensemble works in cooperation in order to control a process or situation. Both the human and the machine cycle through an information processing loop, collecting data, interpreting the situation, deciding on actions to achieve one or more stated goals and acting on the decisions (see for example Coram, 2002;