The difference between analysis automation—inference—anddecision automation is that in the latter the system must make implicit or explicit assump-tions about costs and values inherent in all decisions.

Examples of decisionautomation include route planning and adaptation, such as to avoid bad weather, and systems pro-viding medical diagnosis support.

Decision automation means deciding and selecting appropriate actions among alternatives.This type of automation corresponds to the third human information processing state, decision-making, which the machine is either augmenting or replacing altogether.

An example of low-level automation is the extrapolation or prediction of data over time,such as a system predicting a trend for the output of an industrial plant based on historical sensordata. An example of moderate- to high-level automation is a system integrating multiple sources orinput variables. This could be a display with emergent perceptual features, such as an optical see-through display with a landing strip intended to assist a pilot in landing an aircraft. An exampleof high-level automation is a context-dependent summary of data.

Analysis automation refers to the automation of information analysis and involves inferentialprocesses. It corresponds to the second human information processing state: perception/workingmemory.

An example of low-level automation is assistance in sensor adjustment, such as a system mechanically moving a radarsensor to lock on a detected target. An example of moderate automation is a system organizinginformation according to criteria such as a priority list or highlighting information based on staticor dynamic criteria. This could be, for example, a display highlighting the rate of change in somevariable of interest. This could be indicated by increasing the intensity of some pixels more rapidlythan others in the display.

Acquisition automation corresponds to the first human information processing stage, sensoryprocessing, and it is realized by the system sensing and registering input data.

In all cases, getting the right balance betweenautomation and human control is crucial.

Throughout the history of automation, a central challenge in HCI has been posed by the com-plexity of autonomous systems. Users must be able to understand and control them. They need tofind and integrate information from dynamic and different sources. They need to understand howto delegate tasks, supervise their execution, and intervene if needed.

Automation refers to technology that assists users by performing a task or a subtask on their behalf.

Suchtechniques provide different levels of automation.