Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
Discovering admissible simultaneous equations of large scale systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Applications of Artificial Intelligence for Chemical Inference: The Dendral Project
Applications of Artificial Intelligence for Chemical Inference: The Dendral Project
Determining Arguments of Invariant Functional Descriptions
Machine Learning
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Divide and Conquer Approach to Learning from Prior Knowledge
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Computer-Aided Discovery of Scientific Knowledge
DS '98 Proceedings of the First International Conference on Discovery Science
DS '00 Proceedings of the Third International Conference on Discovery Science
Theory Revision in Equation Discovery
DS '01 Proceedings of the 4th International Conference on Discovery Science
Law discovery using neural networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Automated theory formation in mathematics
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Rediscovering physics with BACON.3
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
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Research on the computational discovery of numeric equations has focused on constructing laws from scratch, whereas work on theory revision has emphasized qualitative knowledge. In this chapter, we describe an approach to improving scientific models that are cast as sets of equations. We review one such model for aspects of the Earth ecosystem, then recount its application to revising parameter values, intrinsic properties, and functional forms, in each case achieving reduction in error on Earth science data while retaining the communicability of the original model. After this, we consider earlier work on computational scientific discovery and theory revision, then close with suggestions for future research on this topic.