Reasoning about causes and consequences in Multilevel Flow Models

The purpose of the paper is to describe how Multilevel Flow Models (MFM) are used for reasoning about causes and consequences in complex dynamic processes. Reasoning in MFM models derives its power from representation of process knowledge on several levels of specification. The detailed specification is the basis for implementation of automated model-based reasoning functions whereas the more abstract specifications provides generic process knowledge for formulation of reasoning strategies and for giving explanations. Reasoning strategies and explanations can be directly visualized in terms of the means-end topology of the multilevel flow models and may be used for design of human machine interfaces supporting diagrammatic reasoning about spatial-temporal aspects of dynamic situations. The principles described in the paper have been used in the implementation of a model based reasoning system.