Abstract
Previous work in model-based reasoninga nd in reasoning about action and change has shown that causal knowledge is essential to perform proper inferences about discrete changes in a system modeled by a set of logical or qualitative constraints.
In this work we show that causal information can also be conveniently used to greatly improve the efficiency of qualitative simulation, prunings purious behaviors and guiding the computation of the “successor” relation, yet maintainingt he ability to deal with ambiguous predictions. The advantages of the approach are demonstrated on test cases, including one from a real application, using a diagnostic engine based on a causaldirected constraint solver.
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Panati, A., Dupré, D.T. (2001). Causal Simulation and Diagnosis of Dynamic Systems. In: Esposito, F. (eds) AI*IA 2001: Advances in Artificial Intelligence. AI*IA 2001. Lecture Notes in Computer Science(), vol 2175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45411-X_15
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DOI: https://doi.org/10.1007/3-540-45411-X_15
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