Model Decomposition and Simulation
Daniel J. Clancy, Benjamin J. Kuipers · 2004
Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. For complex systems containing a large number of variables and constraints, the simulation frequently is intractable or results in a large, incomprehensible behavioral description. Abstraction and aggregation techniques are required during the simulation to eliminate irrelevant details and highlight the important characteristics of the behavior. The total temporal ordering of unrelated events provided by a traditional state-based qualitative representation is one such irrelevant distinction. Model decomposition and simulation addresses this problem. Model decomposition uses a causal analysis of the model to partition the variables into tightly connected components. The components are simulated separately in the order dictated by the causal analysis beginning with causally upstream components. Information from the simulation of causally upstream components is used to c...