Dynamic across-time measurement interpretation
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Monitoring and diagnosis of continuous dynamic systems using semiquantitative simulation
Monitoring and diagnosis of continuous dynamic systems using semiquantitative simulation
Integrating qualitative and quantitative modeling
International Journal of Expert Systems
Qualitative-numeric simulation with Q3
Recent advances in qualitative physics
Qualitative and Quantitative Knowledge in Classical Mechanics
Qualitative and Quantitative Knowledge in Classical Mechanics
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
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Models fall naturally into two main categories, those of discrete systems and those of continuous systems. Our model-based reasoning work deals with continuous systems, augmented to provide for the possibility that one continuous system model transitions to another, as when a threshold event occurs such as a thermostat turning on.One aspect of model-based reasoning is simulation. A model is defined and its behavior(s) inferred through qualitative or numerical simulation. The simulated trajectories then facilitate tasks that one expects model-based reasoning to aid in, such as prediction, monitoring, diagnosis, and design.Here we describe an approach to representing simulation trajectories that results in descriptions of system behavior that contain both qualitative and quantitative information about trajectories closely integrated together. Those descriptions are supported by an internal, representation methodology that also closely integrates qualitative and quantitative information. The internal representation methodology supports quantitative inferences about the trajectories, and an example trace of such inferencing is provided.