ACM Transactions on Mathematical Software (TOMS)
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Modelling of Industrial Systems
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Discovering admissible models of complex systems based on scale-types and identity constraints
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Integrating Domain Knowledge in Equation Discovery
Computational Discovery of Scientific Knowledge
Factors affecting ontology development in ecology
DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
An equation-discovery approach to earthquake-ground-motion prediction
Engineering Applications of Artificial Intelligence
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State of the art equation discovery systems are concerned with the empirical approach to modeling of physical systems, where none or a very limited portion of the expert knowledge about the observed system is used in the modeling process. In this paper, we propose a formalism for integration of the population dynamics modeling knowledge into the process of equation discovery. The formalism allows the encoding of a high-level domain knowledge accessible to human experts. The encoded knowledge can be automatically transformed into the operational form of context dependent grammars. We present an extended version of the equation discovery system Lagramge that can use these context free grammars. Experimental evaluation shows that the integration of domain knowledge in the process of equation discovery considerably improves the efficiency and noise robustness of LAGRAMGE.