functions that cannot be carried out efficiently or in a timely manner by the human operator, as in certain nuclear power plant operations (Sheridan, 1992). Aside from these two cases, however, problems could arise if automation of controller decision making functions—what Hopkin (1995) calls computer assistance—is implemented in such a way that the computer always carries out decisions A and B, and the controller deals with all other decisions. Even this may not be problematic if computer decision making is 100 percent reliable, for then there is little reason for the controller to monitor the computer's inputs, be aware of the details of the traffic pattern that led to the decision, or even, following several years of experience with such a system, know how to carry out that decision manually. As noted in previous sections, however, software reliability for decision making and planning functions is not ensured, so that long-term, fixed automation of such functions could expose the system to human performance vulnerabilities.
Under adaptive automation, the division of labor between human operator and computer systems is flexible rather than fixed. Sometimes a given function may be executed by the human, at other times by automation, and at still others by both the human and the computer. Adaptive automation may involve either task allocation, in which case a given task is performed either by the human or the automation in its entirety, or partitioning, in which case the task is divided into subtasks, some of which are performed by the human and others by the automation. Task allocation or partitioning may be carried out by an intelligent system on the basis of a model of the operator and of the tasks that must be performed (Rouse, 1988). This defines adaptive automation or adaptive aiding. For example, a workload inference algorithm could be used to allocate tasks to the human or to automation so as to keep operator workload within a narrow range (Hancock and Chignell, 1989; Wickens, 1992). Figure 1.5 provides a schematic of how this could be achieved within a closed-loop adaptive system (Wickens, 1992).
An alternative to having an intelligent system invoke changes in task allocation or partitioning is to leave this responsibility to the human operator. This approach defines adaptable automation (Billings and Woods, 1994; Hilburn, 1996). Except where noted, the more generic term adaptive is used here to refer to both cases. Nevertheless, there are significant and fundamental differences between adaptive (machine-directed) and adaptable (human-centered) systems in terms of such criteria as feasibility, ease of communication, user acceptance, etc. Billings and Woods (1994) have also argued that systems with adaptive automation may be more, not less, susceptible to human performance vulnerabilities if they are implemented in such a way that operators are unaware of the states and state changes of the adaptive system. They advocate adaptable automation, in which users can tailor the level and type of automation according to their current needs. Depending on the function that is automated and situation-specific factors (e.g., time pressure, risk, etc.), either adaptive or adaptable automation may be