Comprehending complex behavior graphs through abstraction

Qualitative simulation is often a useful tool for study-ing the behavior of physical systems and has promise for providing automatic explanations of their behav-ior. However, in some cases it can overwhelm with detail. Behavior graphs with hundreds or thousands of states may obscure the basic patterns of behav-ior that a qualitative model was intended to explore. This paper describes an approach to comprehending complex behavior graphs by abstracting the behav-ior graph according to user-specified criteria that are simple and natural to provide. We present proper-ties that an abstraction should meet to be faithful to the original behavior graph, prove necessary and suffi-cient operational conditions for an abstraction method to maintain these properties, and present a simple al-gorithm that incorporates these conditions and works for any behavior graph. We demonstrate on several externally-provided problems that our method can greatly simplify complex behavior graphs in number of states and behaviors while remaining faithful to the original behavior graph. It enables simple graphical output that can reveal underlying patterns of behav-ior that have not been apparent with previous meth-ods, and shows promise for expanding the utility of qualitative reasoning for generating explanations