A Knowledge-based Treatment of Human-Automation Systems
This work addresses the need for precise reasoning about human knowledge in supervisory control systems, which is incremental as it adapts an existing formal method to a new domain.
The paper tackles the problem of modeling and analyzing complex human-automation systems by applying a formal approach to reason about agent knowledge, with results demonstrating the validity and value of this method in a case study from the literature.
In a supervisory control system the human agent knowledge of past, current, and future system behavior is critical for system performance. Being able to reason about that knowledge in a precise and structured manner is central to effective system design. In this paper we introduce the application of a well-established formal approach to reasoning about knowledge to the modeling and analysis of complex human-automation systems. An intuitive notion of knowledge in human-automation systems is sketched and then cast as a formal model. We present a case study in which the approach is used to model and reason about a classic problem from the human-automation systems literature; the results of our analysis provide evidence for the validity and value of reasoning about complex systems in terms of the knowledge of the system agents. To conclude, we discuss research directions that will extend this approach, and note several systems in the aviation and human-robot team domains that are of particular interest.