Artificial Intelligence
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Synergy between Compositional Modeling and Bayesian Networks
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
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Inferring causality from equation models characterizing engineering domains is important towards predicting and diagnosing system behavior. Most previous attempts in this direction have failed to recognize the key differences between equations which model physical phenomena and those that just express rationality or numerical conveniences of the designer. These different types of equations bear different causal implications among the model parameters they relate. We show how unstructured and ad hoc formulations of equation models for apparent numerical conveniences are lossy in the causal information encoding and justify the use of CML as a model formulation paradigm which retains these causal structures among model parameters by clearly separating equations corresponding to phenomena and rationality. We provide an algorithm to infer causality from the active model fragments by using the notion of PreCondition graphs.