Abstract
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.
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References
Chown, E., Dietterich, T.G.: A divide and conquer approach to learning from prior knowledge. In: Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, pp. 143–150 (2000)
Durbin, R., Rumelhart, D.E.: Product units: A computationally powerful and biologically plausible extension. Neural Computation 1, 133–142 (1989)
Kokar, M.M.: Determining arguments of invariant functional descriptions. Machine Learning 1, 403–422 (1986)
Langley, P.: Rediscovering physics with Bacon.3. In: Proceedings of the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, pp. 505–507 (1979)
Langley, P.: The computer-aided discovery of scientific knowledge. In: Proceedings of the First International Conference on Discovery Science. Fukuoka, Japan (1998)
Langley, P., Simon, H.A., Bradshaw, G.L., Żytkow, J.M.: Scientific discovery: Computational explorations of the creative processes. MIT Press, Cambridge, MA (1987)
Lenat, D.B.: Automated theory formation in mathematics. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA, pp. 833–842 (1977)
Lindsay, R.K., Buchanan, B.G., Feigenbaum, E.A., Lederberg, J.: Applications of artificial intelligence for organic chemistry: The Dendral project. McGraw-Hill, New York (1980)
Ourston, D., Mooney, R.: Changing the rules: A comprehensive approach to theory refinement. In: Proceedings of the Eighth National Conference on Artificial Intelligence, Boston, MA, pp. 815–820 (1990)
Potter, C.S., Klooster, S.A.: Global model estimates of carbon and nitrogen storage in litter and soil pools: Response to change in vegetation quality and biomass allocation. Tellus 49B, 1–17 (1997)
Potter, C.S., Klooster, S.A.: Interannual variability in soil trace gas (CO2, N2O, NO) fluxes and analysis of controllers on regional to global scales. Global Biogeochemical Cycles 12, 621–635 (1998)
Saito, K., Nakano, R.: Law discovery using neural networks. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, pp. 1078–1083 (1997)
Saito, K., Nakano, R.: Discovery of nominally conditioned polynomials using neural networks, vector quantizers and decision trees. In: Proceedings of the Third International Conference on Discovery Science, Kyoto, Japan, pp. 325–329 (2000)
Schwabacher, M., Langley, P.: Discovering communicable scientific knowledge from spatio-temporal data. In: Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA, pp. 489–496 (2001)
Todorovski, L., Džeroski, S.: Declarative bias in equation discovery. In: Proceedings of the Fourteenth International Conference on Machine Learning, Nashville, TN, pp. 376–384 (1997)
Todorovski, L., Dzeroski, S.: Theory revision in equation discovery. In: Proceedings of the Fourth International Conference on Discovery Science, Washington, D.C., pp. 389–400 (2001)
Towell, G.: Symbolic knowledge and neural networks: Insertion, refinement, and extraction. Doctoral dissertation, Computer Sciences Department, University of Wisconsin, Madison (1991)
Washio, T., Motoda, H.: Discovering admissible simultaneous equations of large scale systems. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, WI, pp. 189–196 (1998)
Żytkow, J.M., Zhu, J., Hussam, A.: Automated discovery in a chemistry laboratory. In: Proceedings of the Eighth National Conference on Artificial Intelligence, Boston, MA, pp. 889–894 (1990)
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Saito, K., Langley, P. (2007). Quantitative Revision of Scientific Models. In: Džeroski, S., Todorovski, L. (eds) Computational Discovery of Scientific Knowledge. Lecture Notes in Computer Science(), vol 4660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73920-3_6
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DOI: https://doi.org/10.1007/978-3-540-73920-3_6
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