A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Artificial Intelligence
Qualitative simulation as a temporally-extended constraint satisfaction problem
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Non Binary CSPs and Heuristics for Modeling and Diagnosing Dynamic Systems
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Embracing causality in specifying the indirect effects of actions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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 causal-directed constraint solver.