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
Qualitative analysis of MOS circuits
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Intelligence in scientific computing
Communications of the ACM
The qualitative process engine
Readings in qualitative reasoning about physical systems
The Dynamicist''s Workbench: I ---Automatic Preparation of Numerical Experiments
The Dynamicist''s Workbench: I ---Automatic Preparation of Numerical Experiments
A Compilation Strategy for Numerical Programs Based on Partial Evaluation
A Compilation Strategy for Numerical Programs Based on Partial Evaluation
Introducing actions into qualitative simulation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Scaling up self-explanatory simulators polynomial time compilation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Using modeling knowledge to guide design space search
Artificial Intelligence
Simulation-based temporal projection of everyday robot object manipulation
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Qualitative modeling as a paradigm for diagnosis and prediction in critical care environments
Artificial Intelligence in Medicine
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A central goal of qualitative physics is to provide a framework for organizing and using quantitative knowledge. One important use of quantitative knowledge is numerical simulation. While current numerical simulators are powerful, they are often hard to construct, do not reveal the assumptions underlying their construction, and do not produce explanations of the behaviors they predict. This paper shows how to combine qualitative and quantitative models to produce a new class of self-explanatory simulations which combine the advantages of both kinds of reasoning. Self-explanatory simulations provide the accuracy of numerical models and the interpretive power of qualitative reasoning. We define what self-explanatory simulations are and show how to construct them automatically. We illustrate their power with some examples generated with an implemented system, SIMGEN. We analyze the limitations of our techniques, and discuss plans for future work.