Automation often has been implemented in work systems in the past without use of well-founded theoretical frameworks which thoroughly account for the overall performance demand that a work system (including the human operator) as a whole should be designed for, i.e. to comply best with the work objective. The lack of a theoretically-founded systematic approach is the reason why the potential of conventional automation has been overestimated for a long time by system engineers and that, on the other hand, ergonomists often underestimated the potential of automation as such. We hypothesise at this point that the crucial technological step to avoid the drawbacks of conventional automation is the introduction of artificial cognition in terms of cognitive automation. This allows to deal with higher degrees of complexity the same way as humans can brilliantly do, based on motivational contexts. Higher automation complexity certainly is the price to pay for making possible significant gains in productivity of work systems, but we will realise in the following of this article that in fact cognitive automation can pave the way towards these gains without loosing much as it is the case for conventional automation. Therefore, it is a crucial issue in work system design to develop a methodological approach for exploiting the automation potential in the most beneficial manner and therefore making use of artificial cognition by means of cognitive automation in addition to conventional automation.
By the way, this can be considered as the major issue of this article. How this can be achieved by a more systematic design strategy will become evident in the following. First, though, we shall clarify what cognitive automation means in distinction to conventional automation before describing the benefits of it and the design requirements to make use of it in a work system. Also in case of cognitive automation, we also have to deal with both ways of automation, ACUs as operator-controlled or built-in automation. Taking again the functional model of human cognitive behaviour as a reference, everything what includes a functional component of feature formation as opposed to the pure data processing of conventional automation and what goes otherwise beyond conventional automation in terms of replacing the human operator for the cognitive functions of goal and task determination, will be accounted for in the following as cognitive automation. Thereby, the goal determination is based on a-priori knowledge of motivational contexts and the input of the identification function, whereas the task determination relies on apriori knowledge of task situations. At the same time a corresponding functional component of feature formation is evolving. At the low end, only a task determination function is involved. At its best, cognitive automation comprises the whole set of functions. The more knowledge is involved, the more comprehensive are the cognitive capabilities. This indicates that theoretically there is an unlimited potential for increase of performance. In summary, cognitive automation stands for artificial capabilities
• to understand the situation in case of unforeseen events and to independently interpret it in the light of the known motivational contexts as drivers for voluntary actions,
• to develop an understanding of the necessary sequence of actions bestsuited to accomplish the desired result according to the assignment, thereby distinguishing between important and unimportant information, urgent and less urgently needed actions,
• to perform those actions which are authorised by its assignment, and
• to effectively initiate the necessary communication to other units of the pertinent work environment, thereby evening up how to proceed in case of conflicts and opportunities.
These are the cognitive capabilities, in particular that of independently interpreting the work situation in the light of known own motivational contexts, whose potential for the work system design will be investigated further in the following. In that sense, we consider in the following of this article cognitive automation as being realised in terms of a particular kind of agents which have got these capabilities and which we call artificial cognitive units (ACUs). Comparing ACUs with humans, we make use of the option in the following that ACUs are merely components analogue to what is meant by the human cognition as the inner functional layer of the human operator. Of course, also the outer layer with sensing and effecting means is necessary for the ACU, too, in order to make the ACU an effective player in the work system. However, in distinction to humans, these could typically also be provided by corresponding separate operation-supporting means as being true for many operational systems. In order to make appropriate use of these means the ACU has got detailed knowledge about their capabilities and functionality.
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