Towards models of tasks and task complexity in supervisory control applications

Dynamic task environments in supervisory control situations differ from those traditionally investigated in problem-solving research in that (1) several task goals exist in parallel, (2) task goals change dynamically as the behaviour of the technical process changes, and (3) information required to accomplish task goals changes across time. In the present work, it is suggested that such dynamic task environments can be described using two types of task goal networks, namely a control task goal (CTG) network and an information processing goal (IPG) network. CTG networks are generated by analysis of the operational states required to produce the commodity for which a technical system has been designed. For example, such analyses can be performed using approaches such as Mitchell's operator function model or canonical means-end analyses. IPG networks are generated by using the recently proposed functional information and knowledge acquisition (FIKA) modelling technique. Two examples from different domains illustrate how these task goals networks can be used to describe dynamic task environments. Finally, two different ways of using the task modelling approach are briefly discussed.