A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Higher-order derivative constraints in qualitative simulation
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
The composition and validation of heterogeneous control laws
Automatica (Journal of IFAC)
An artificial intelligence approach to multi-level mixed-mode qualitative simulation of CMOS ICs
Computers and Electrical Engineering
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Computational systems for qualitative economics
Computational Economics
Qualitative system identification: deriving structure from behavior
Artificial Intelligence
Proving properties of continuous systems: qualitative simulation and temporal logic
Artificial Intelligence
A non-parametric Monte Carlo technique for controller verification
Automatica (Journal of IFAC)
International Journal of Robotics Research
Solving complexity and ambiguity problems within qualitative simulation
Solving complexity and ambiguity problems within qualitative simulation
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We subject a basic qualitative modelwhich appears throughout the qualitativereasoning literature (the ``bathtub'' or liquidtank model) to a detailed theoretical analysisof its representation properties. We show thatthe standard model for this family of systemsdoes not cover the intuitive concept ofreal-world tanks, in that there are both simpletanks that do not obey the model, and thatthere are physically impossible shapes that doobey it and get considered by qualitativereasoners using the model. We demonstrate thatthese modeling problems may lead to a markeddecrease in the usefulness of employingqualitative reasoners in some cases. Weconclude that one should be careful aboutmaking both the assumptions required by themodel and the algorithm, and the family ofsystems that are actually reasoned about,explicit in the presentation of qualitativereasoners' output.