Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Modern control theory (3rd ed.)
Modern control theory (3rd ed.)
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Automatic control systems (7th ed.)
Automatic control systems (7th ed.)
Tasks and ontologies in engineering modelling
International Journal of Human-Computer Studies
Reasoning about nonlinear system identification
Artificial Intelligence
Linear Control Systems
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Automatic Qualitative Modeling of Dynamic Physical Systems
Automatic Qualitative Modeling of Dynamic Physical Systems
Automating input-output modeling of dynamic physical systems
Automating input-output modeling of dynamic physical systems
Generalized physical networks for automated model building
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A formal hybrid modeling scheme for handling discontinuities in physical system models
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Automated mathematical modeling from experimental data: anapplication to material science
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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We present a new knowledge representation and reasoning framework for modeling nonlinear dynamic systems. The goals of this framework are to smoothly incorporate varying levels of domain knowledge and to tailor the search space and the reasoning methods accordingly. In particular, we introduce a new structure for automated model building known as a meta-domainwhich, when instantiated with domain-specific components, tailors the space of candidate models to the system at hand. We combine this abstract modeling paradigm with ideas from generalized physical networks, a meta-level representation of idealized two-terminal elements, and a hierarchy of qualitative and quantitative analysis tools, to produce dynamic modeling domains whose complexity naturally adapts to the amount of available information about the target system. Since the domain and meta-domain representation use the same type of techniques and formalisms as practicing engineers, the models produced from these frameworks are naturally communicable to their target audience.