Decision Demands and Task Requirements in Work Environments: What Can be Learnt from Human Operator Modelling

Summary

Supervisory control can be distinguished from manual control which is a closed loop, skill based action, by four important aspects:

• The control of highly complex, multivariable processes with mostly large to very large time constants.

• The discrete action patterns based on decision making processes.

• The variability in tasks, such as process tuning, start and stop procedures and fault management.

• The often vague information on the ultimate supervisory control perspectives. Hence, supervisory control tasks are different from those in manual control. Taking into account the three major process control modes which may occur in supervisory control -Normal operation, start and stop, and abnormal operation-it is of interest to classify the different tasks with reference to these control modes, as well as with reference to the three levels of control behavior as introduced by Rasmussen: Skill-based, Rule-based and Knowledge-based behavior. At the Skill-based level only manual control activities play a role; at the Rule-based level activities like process tuning and to a certain extent fault management may occur, whereas at the Knowledge-based level intelligent, cognitive activities such as optimisation, planning and fault management are thought to be.

In understanding at which level of human control the different tasks can be placed best, it is instructive to study where successful human operator models are reported in literature. At the Skill-based level certainly successful control models describing manual control behavior have been reported. At the Rule-based level, less but still to a certain extent, some control and artificial intelligence models are known. At the Knowledge-based level, tasks like fault management can only be modelled, if and only if, these tasks are that well defined that they are far away from real world situations; hence only very few models have been published. So, the conclusion can be drawn that only in those cases where tasks are well defined, successful human operator models are developed. This important statement elucidates exactly the basic problem:

The tasks which yield a correct description of human supervisory behavior are to be find at the Skill- and Rule-based level, they are all well defined; those which do not lead to any description of human behavior are Knowledge-based level tasks, and they are not well defined. In particular, the last class of tasks is dealing with those tasks where one needs the operator for his creativity, knowledge and intelligence.

As a concluding remark the following can be said. The human operator should mainly be the adaptive, creative and intelligent supervisor who does things which can not easily or at all expected. So, those tasks which can be carefully defined, thus where modelling of the human behavior is expected to be successful, can easily be taken away from the operator. Often intelligent MMIf can help the operator to support him to do the job. Whether this is wise or not is dependent on factors such as the need for training, for learning, for building up an Internal Representation of the process, etc. So the final and major question becomes: to what extent should one automate, or, to what extent should one build in artificial intelligence in man-machine system interfaces?