Will Bridewell
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.
- R. Arditi and L. R. Ginzburg. Coupling in predator-prey dynamics - ratio--dependence. Journal of Theoretical Biology, 139:311--326, 1989.Google Scholar
Cross Ref
- W. Bridewell and L. Todorovski. Learning declarative bias. Proceedings of the Seventeenth International Conference on Inductive Logic Programming, to appear, 2007. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- S. Cohen and A. Hindmarsh. CVODE, a stiff/nonstiff ODE solver in C. Computers in Physics, 10:138--143, 1996. Google ScholarDigital Library
- L. Dehaspe and H. Toivonen. Discovery of relational association rules. In Relational Data Mining, pages 189--212. Springer, Berlin, Germany, 2001. Google ScholarCross Ref
- A. M. Edwards. Adding detritus to a nutrient-phytoplankton-zooplankton model: A dynamical systems approach. Journal of Plankton Research, 23:389--413, 2001.Google ScholarCross Ref
- B. Falkenhainer and K. D. Forbus. Compositional modeling: Finding the right model for the job. Artificial Intelligence, 51:95--143, 1991. Google ScholarDigital Library
- 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/.Google Scholar
- C. Giraud-Carrier, R. Vilalta, and P. Brazdil. Introduction to the special issue on meta-learning. Machine Learning, 54:187--193, 2004. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, 1994. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- J. H. Martin, R. M. Gordon, and S. E. Fitzwater. The case for iron. Limnology and Oceanography, 36:1793--1802, 1991.Google ScholarCross Ref
- 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.Google Scholar
- S. Muggleton. Learning from positive data. In Proceedings of the Sixth International Workshop on Inductive Logic Programming, pages 358--376, Stockholm, Sweden, 1996. Springer. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- M. J. Pazzani and D. F. Kibler. The utility of knowledge in inductive learning. Machine Learning, 9:57--94, 1992. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- A. Srinivasan. The Aleph Manual. Computing Laboratory, Oxford University, 2000.Google Scholar
- J. H. Steele and E. W. Henderson. A simple plankton model. American Naturalist, 117:676--691, 1981.Google ScholarCross Ref
- A. Tagliabue and K. R. Arrigo. Anomalously low zooplankton abundance in the Ross Sea: An alternative explanation. Limnology and Oceanography, 48:686--699, 2003.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- J. M. Zytkow. Model construction: Elements of a computational mechanism. In AISB'99 Symposium on AI and Scientific Creativity, Edinburgh, Scotland, 1999.Google Scholar
Index Terms
- Extracting constraints for process modeling
Recommendations
Constructing explanatory process models from biological data and knowledge
Objective: We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. Methods: We cast both models and background ...
Discovering constraints for inductive process modeling
AAAI'12: Proceedings of the Twenty-Sixth AAAI Conference on Artificial IntelligenceScientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combinations of these components. Previous research on inductive process ...
Incremental Process Modeling through Stakeholder-Based Hybrid Process Simulation
ICSP '09: Proceedings of the International Conference on Software Process: Trustworthy Software Development ProcessesBoth the process modeling and process simulation are necessary components of process automation. A Process Modeling Language (PML) is a set of description tools that define processes attributes and constrains in a specific domain. Process modeling ...
Comments