Reasoning about nonlinear system identification
Artificial Intelligence
Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Reasoning about Input-Output Modeling of Dynamical Systems
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Intelligent Sensor Analysis and Actuator Control
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Qualitative simulation and related approaches for the analysis of dynamic systems
The Knowledge Engineering Review
Communicable Knowledge in Automated System Identification
Computational Discovery of Scientific Knowledge
Incorporating Engineering Formalisms into Automated Model Builders
Computational Discovery of Scientific Knowledge
Spatial aggregation for qualitative assessment of scientific computations
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Generalized physical networks for automated model building
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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Automated model formulation is a crucial issue in the construction of computational environments that can reason about the behavior of a physical system. The procedure of mathematically modeling a physical system is complex and involves three fundamental entities: the experimental data, a set of candidate models, and rules for determining in such a set the “best” model that reproduces the measured data. The construction of the candidate models is domain dependent and based on specific knowledge and techniques of the application domain. The choice of the best model is guided by the data themselves; a first rough guess is refined through system identification techniques so that the quantitative properties of the observed behavior are assessed. Automating such a procedure requires handling and integrating different formalisms and methods, both qualitative and quantitative. The paper describes a comprehensive environment that aims at the automated formulation of an accurate quantitative model of the mechanical behavior of an actual viscoelastic material in accordance with the observed response of the material to standard experiments. Algorithms and methods for both the generation of an exhaustive library of models of ideal materials and the selection of the most “accurate” model of a real material have been designed and implemented. The model selection phase occurs in two main stages: first the subset of most plausible candidate models for the material is drawn from the library; then, the most accurate model of the material is identified by using both statistical and numerical methods