Cited By
View all- Bridewell WLangley P(2010)Two Kinds of Knowledge in Scientific DiscoveryTopics in Cognitive Science10.1111/j.1756-8765.2009.01050.x2:1(36-52)Online publication date: 13-Jan-2010
Extracting constraints for process modeling
Pages 87 - 94
Abstract
In this paper, we introduce an approach for extracting constraints on process model construction. We begin by clarifying the type of knowledge produced by our method and how one may apply it. Next, we reviewthe task of inductive process modeling, which provides the required data. We then introduce a logical formalismand a computational method for acquiring scientific knowledge from candidate process models. Results suggestthat the learned constraints make sense ecologically and may provide insight into the nature of the modeled domain. We conclude the paper by discussing related and future work.
References
[1]
R. Arditi and L. R. Ginzburg. Coupling in predator-prey dynamics - ratio--dependence. Journal of Theoretical Biology, 139:311--326, 1989.
[2]
W. Bridewell and L. Todorovski. Learning declarative bias. Proceedings of the Seventeenth International Conference on Inductive Logic Programming, to appear, 2007.
[3]
W. Bridewell, J. Sanchez, P. Langley, and D. Billman. An interactive environment for the modeling and discovery of scientific knowledge. International Journal of Human--Computer Studies, 64:1099--1114, 2006.
[4]
D. S. Bunch, D. M. Gay, and R. E. Welsch. Algorithm 717: Subroutines for maximum likelihood and quasi--likelihood estimation of parameters in nonlinear regression models. ACM Transactions on Mathematical Software, 19:109--130, 1993.
[5]
S. Cohen and A. Hindmarsh. CVODE, a stiff/nonstiff ODE solver in C. Computers in Physics, 10:138--143, 1996.
[6]
L. Dehaspe and H. Toivonen. Discovery of relational association rules. In Relational Data Mining, pages 189--212. Springer, Berlin, Germany, 2001.
[7]
A. M. Edwards. Adding detritus to a nutrient-phytoplankton-zooplankton model: A dynamical systems approach. Journal of Plankton Research, 23:389--413, 2001.
[8]
B. Falkenhainer and K. D. Forbus. Compositional modeling: Finding the right model for the job. Artificial Intelligence, 51:95--143, 1991.
[9]
S. Frohler and S. Kramer. Logic-based information integration and machine learning for gene regulation prediction. In Proceedings of the Ninth International Conference on Molecular Systems Biology, 2006. Available online at http://www.icmsb06.org/.
[10]
C. Giraud-Carrier, R. Vilalta, and P. Brazdil. Introduction to the special issue on meta-learning. Machine Learning, 54:187--193, 2004.
[11]
K. L. Haberman, R. M. Ross, L. B. Quetin, M. Vernet, G. A. Nevitt, and W. Kozlowski. Grazing by antarctic krill Euphausia superba on Phaeocystis antarctica: An immunochemical approach. Marine Ecology Progress Series, 241:139--149, 2002.
[12]
M. P. Hassell and G. C. Varley. New inductive population model for insect parasites and its bearing on biological control. Nature, 223:1133--1137, 1969.
[13]
C. S. Holling. The components of predation as revealed by a study of small mammal predation of the European pine sawfly. Canadian Entomologist, 91:293--320, 1959.
[14]
Y. Huang, B. Selman, and H. A. Kautz. Learning declarative control rules for constraint--based planning. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 415--422, Stanford, CA, 2000. Morgan Kaufmann.
[15]
C. Jost and S. P. Ellner. Testing for predator dependence in predator-prey dynamics: A non-parametric approach. Proceedings of the Royal Society of London Series B-Biological Sciences, 267:1611--1620, 2000.
[16]
P. Langley, J. Sanchez, L. Todorovski, and S. Dzeroski. Inducing process models from continuous data. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 347--354, Sydney, 2002. Morgan Kaufmann.
[17]
N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, 1994.
[18]
A. Mahidadia and P. Compton. Assisting model-discovery in neuroendocrinology. In Proceedings of the Fourth International Conference on Discovery Science, pages 214--227, Washington DC, 2001. Springer.
[19]
J. H. Martin, R. M. Gordon, and S. E. Fitzwater. The case for iron. Limnology and Oceanography, 36:1793--1802, 1991.
[20]
E. McCreath and A. Sharma. Extraction of meta-knowledge to restrict the hypothesis space for ILP systems. In Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pages 75--82, Canberra, Australia, 1995. World Scientific Publishers.
[21]
S. Muggleton. Learning from positive data. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, pages 358--376, Stockholm, Sweden, 1996. Springer.
[22]
R. J. Olson, H. M. Sosik, A. M. Chekalyuk, and A. Shalapyonok. Effects of iron enrichment on phytoplankton in the Southern Ocean during late summer: Active fluorescence and flow cytometric analyses. Deep-Sea Research Part II-Topical Studies in Oceanography, 47:3181--3200, 2000.
[23]
M. J. Pazzani and D. F. Kibler. The utility of knowledge in inductive learning. Machine Learning, 9:57--94, 1992.
[24]
V. Schoemann, S. Becquevort, J. Stefels, W. Rousseau, and C. Lancelot. Phaeocystis blooms in the global ocean and their controlling mechanisms: A review. Journal of Sea Research, 53:43--66, 2005.
[25]
A. Srinivasan. The Aleph Manual. Computing Laboratory, Oxford University, 2000.
[26]
J. H. Steele and E. W. Henderson. A simple plankton model. American Naturalist, 117:676--691, 1981.
[27]
A. Tagliabue and K. R. Arrigo. Anomalously low zooplankton abundance in the Ross Sea: An alternative explanation. Limnology and Oceanography, 48:686--699, 2003.
[28]
L. Todorovski, W. Bridewell, O. Shiran, and P. Langley. Inducing hierarchical process models in dynamic domains. In Proceedings of the Twentieth National Conference on Artificial Intelligence, pages 892--897, Pittsburgh, PA, 2005. AAAI Press.
[29]
J. M. Zytkow. Model construction: Elements of a computational mechanism. In AISB'99 Symposium on AI and Scientific Creativity, Edinburgh, Scotland, 1999.
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- Extracting constraints for process modeling
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Published: 28 October 2007
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K-CAP07: International Conference on Knowledge Capture 2007
October 28 - 31, 2007
BC, Whistler, Canada
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View all- Bridewell WLangley P(2010)Two Kinds of Knowledge in Scientific DiscoveryTopics in Cognitive Science10.1111/j.1756-8765.2009.01050.x2:1(36-52)Online publication date: 13-Jan-2010
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