Causal reasoning based on MFM

This paper presents a method for representing more precisely the actual causal structure of the system being modeled, directly in MFM, based on a set of generic relations, which can be used to make explicit the causal relations hidden in the MFM connection relation.

Abstract

There seems to be a great potential for using Multilevel Flow Modeling as a framework for reasoning in supervisory control of complex systems. It is a precondition, however, that knowledge about the causal relations between flow functions is represented. Previous attempts have used generic causation rules, specifying possible influences between specific types of flow functions. The problem with this approach is that the generic nature of such rules sometimes leads to invalid reasoning results. This paper presents a method for representing more precisely the actual causal structure of the system being modeled, directly in MFM. The method is based on a set of generic relations, which can be used to make explicit the causal relations hidden in the MFM connection relation. Implications of the method are illustrated by means of simple examples. 1. Introduction In the field of Cognitive Systems Engineering a great deal of research has been concerned with the development of operator support...