Theory refinement combining analytical and empirical methods
Artificial Intelligence
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Computational Revision of Quantitative Scientific Models
DS '01 Proceedings of the 4th International Conference on Discovery Science
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
Revising Engineering Models: Combining Computational Discovery with Knowledge
ECML '02 Proceedings of the 13th European Conference on Machine Learning
An interactive environment for scientific model construction
Proceedings of the 2nd international conference on Knowledge capture
An interactive environment for the modeling and discovery of scientific knowledge
International Journal of Human-Computer Studies
Integrating Domain Knowledge in Equation Discovery
Computational Discovery of Scientific Knowledge
Quantitative Revision of Scientific Models
Computational Discovery of Scientific Knowledge
Learning in rich representations: inductive logic programming and computational scientific discovery
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Inductive queries on polynomial equations
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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State of the art equation discovery systems start the discovery process from scratch, rather than from an initial hypothesis in the space of equations. On the other hand, theory revision systems start from a given theory as an initial hypothesis and use new examples to improve its quality. Two quality criteria are usually used in theory revision systems. The first is the accuracy of the theory on new examples and the second is the minimality of change of the original theory. In this paper, we formulate the problem of theory revision in the context of equation discovery. Moreover, we propose a theory revision method suitable for use with the equation discovery system Lagramge. The accuracy of the revised theory and the minimality of theory change are considered. The use of the method is illustrated on the problem of improving an existing equation based model of the net production of carbon in the Earth ecosystem. Experiments show that small changes in the model parameters and structure considerably improve the accuracy of the model.