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Granularity and disaggregation in compositional modelling with applications to ecological systems

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Abstract

In the past decade, compositional modelling (CM) has established itself as the predominant knowledge-based approach to construct mathematical (simulation) models automatically. Although it is mainly applied to physical systems, there is a growing interest in applying CM to other domains, such as ecological and socio-economic systems. Inspired by this observation, this paper presents a method for extending the conventional CM techniques to suit systems that are fundamentally presented by interacting populations of individuals instead of physical components or processes. The work supports building model repositories for such systems, especially in addressing the most critical outstanding issues of granularity and disaggregation in ecological systems modelling.

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References

  1. Bobrow D, Falkenhainer B, Farquhar A, Fikes R, Forbus KD, Gruber TR, Iwasaki Y, Kuipers BJ (1996) A compositional modeling language. In: Proceedings of the 10th International Workshop on Qualitative Reasoning about Physical Systems, pp 12–21

  2. Bradley E, Easley M, Stolle R (2001) Reasoning about nonlinear system identification. Artif Intell 133:139–188

    Article  MATH  Google Scholar 

  3. Brajnik G, Lines M (1998) Qualitative modeling and simulation of socio-economic phenomena. J Artif Soc Social Simul 1

  4. Bunn DW, Larsen ER (1992) Sensitivity of reserve margin to factors influencing investment in the UK electricity market. Energy Policy 20(5):490–502

    Google Scholar 

  5. Dangerfield B, Fang Y, Roberts C (2001) Model-based scenarios for the epidemiology of hiv/aids: the consequences of highly active antiretroviral therapy. Syst Dyn Rev 17(2):119–150

    Article  Google Scholar 

  6. de Kleer J (1986) An assumption-based TMS. Artif Intell 28:127–162

    Article  Google Scholar 

  7. de Kleer J (1988) A general labeling algorithm for assumption-based truth maintenance. In: Proceedings of the 7th National Conference on Artificial Intelligence, pp 188–192

  8. de Kleer J (1989) A comparison of ATMS and CSP techniques. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp 290–296

  9. Falkenhainer B, Forbus K (1991) Compositional modeling: finding the right model for the job. Artif Intell 51:95–143

    Article  Google Scholar 

  10. Ferguson BS, Lim GC (1998) Introduction to dynamic economic models. Manchester University Press

  11. Ford A (1999) Modeling the Environment—An Introduction to System Dynamics Modeling of Environmental Systems. Island Press

  12. Forrester JW (1968) Principles of systems. Wright-Allen Press, Cambridge, MA, USA

    Google Scholar 

  13. Gottschalk P (1983) A system dynamics model for long range planning in a railroad. Eur J Oper Res 14(2):156–162

    Article  MathSciNet  Google Scholar 

  14. Hamscher W, Console L, de Kleer J (eds) (1992) Readings in Model-Based Diagnosis. Morgan-Kaufmann, San Mateo

    Google Scholar 

  15. Hastings A (1997) Population biology. Concepts and models. Springer-Verlag, New York

    MATH  Google Scholar 

  16. Heller U, Struss P (1998) Diagnosis and therapy recognition for ecosystems—usage of model-based diagnosis techniques. In: Proceedings of the 12th International Symposium on Computer Science for Environment Protection

  17. Heller U, Struss P (2001) Transformation of qualitative dynamic models—application in hydro-ecology. In: Hotz L, Struss P, Guckenbienl T (eds) Intelligent Diagnosis in Industrial Applications, Shaker Verlag, pp 95–106.

  18. Holling CS (1959) Some characteristics of simple types of predation and parasitism. Canadian Entomol 91:385–398

    Article  Google Scholar 

  19. Ittig PT (1976) A model for planning ambulatory health services. Manag Sci 24(10):1001–1010

    Article  Google Scholar 

  20. Keppens J, Shen Q (2001) On compositional modelling. Knowl Eng Rev 16(2):157–200

    Article  MATH  Google Scholar 

  21. Keppens J, Shen Q (2004) Compositional model repositories via dynamic constraint satisfaction with order-of-magnitude preferences. J Artif Intell Res 21:499–550

    MATH  MathSciNet  Google Scholar 

  22. Kuipers BJ (1993) Reasoning with qualitative models. Artif Intell 59:125–132

    Article  Google Scholar 

  23. Langley P, Sanchez J, Todorovski L, Džeroski S (2002) Inducing process models from continuous data. In: Proceedings of the 19th International Conference on Machine Learning, pp 347–354

  24. Leitch RR, Shen Q, Coghill GM, Chantler MJ (1999) Choosing the right model. IEE Proceedings on Control Theory and Applications 146:435–449

    Article  Google Scholar 

  25. Levy AY, Iwasaki Y, Fikes R (1997) Automated model selection for simulation based on relevance reasoning. Artif Intell 96:351–394

    Article  MATH  MathSciNet  Google Scholar 

  26. Malthus TR (1798) An essay on the principle of population. Printed for J. Johnson in St. Paul’s Church Yard, London, England

    Google Scholar 

  27. Mittal S, Falkenhainer B (1990) Dynamic constraint satisfaction problems. In: Proceedings of the 8th National Conference on Artificial Intelligence, pp 25–32

  28. Nayak PP, Joskowicz L (1996) Efficient compositional modeling for generating causal explanations. Artif Intell 83:193–227

    Article  Google Scholar 

  29. Rickel J, Porter B (1997) Automated modeling of complex systems to answer prediction questions. Artif Intell 93:201–260

    Article  MATH  MathSciNet  Google Scholar 

  30. Salles P, Bredeweg B (2002) A case study of collaborative modelling: building qualitative models in ecology. In: Bredeweg B (ed) Proceedings of the International workshop on Model-based Systems and Qualitative Reasoning for Intelligent Tutoring Systems, pp 75–84

  31. Stein J, Louca L (1996) A component-based modeling approach for system design: theory and implementation. Trans Soc Comput Simul Int 13(2):87–101

    Google Scholar 

  32. Struss P (1998) Artificial intelligence for nature—why knowledge representation and problem solving should play a key role in environmental decision support. In: Haasis HD, Ranze KC (eds) Computer Science for the Environmental Protection ’98. Metropolis Verlag

  33. Tiller M (2001) Introduction to Physical Modeling With Modelica, volume 615 of International Series in Engineering and Computer Science. Kluwer

  34. Todorovski L, Džeroski S (2001) Using domain knowledge on population dynamics modeling for equation discovery. In: Proceedings of the 12th European Conference on Machine Learning, pp 478–490

  35. van Ackere A, Smith P (1999) Towards a macro model of national health service waiting lists. Syst Dyn Rev 15(3):225–252

    Article  Google Scholar 

  36. Verhulst P (1838) Recherches mathématiques sur la loi d’accroisse- ment de la population. Nouveaux mémoires de l’académie royale des sciences et belles-lettres de Bruxelles 18:1–38

    Google Scholar 

  37. Waltz D (1975) Understanding line drawings of scenes with shadows. In: Winston P (ed) The Psychology of Computer Vision, McGraw-Hill, pp 19–91

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Authors and Affiliations

  1. Department of Computer Science, King’s College London, Strand, London, WC2R 2LS, UK

    Jeroen Keppens

  2. Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB, UK

    Qiang Shen

Authors
  1. Jeroen Keppens
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  2. Qiang Shen
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Correspondence to Qiang Shen.

Additional information

Jeroen Keppens is a lecturer in the Department of Computer Science at King’s College London, working in the Software Engineering Group. His research interests include Approximate and Qualitative Reasoning, Model Based Reasoning, Automated Model Construction and Applications of Artificial Intelligence in Law and Ecological Modelling. Dr. Keppens has published around 25 peer reviewed publications in these areas.

Qiang Shen is a Professor and the Director of Research with the Department of Computer Science at the University of Wales, Aberystwyth, UK. He is also an Honorary Fellow at the University of Edinburgh, UK. His research interests include fuzzy systems, knowledge modelling, qualitative reasoning, and pattern recognition. Prof. Shen serves as an associate editor or editorial board member of a number of world leading journals, including the IEEE Transactions on Systems, Man, and Cybernetics (Part B), the IEEE Transactions on Fuzzy Systems, and Fuzzy Sets and Systems. He has acted as a Chair or Co-chair at a good number of major conferences in the field of Computational Intelligence. He has published a book and over 170 peer-refereed articles in international journals and conferences in Artificial Intelligence and related areas.

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Keppens, J., Shen, Q. Granularity and disaggregation in compositional modelling with applications to ecological systems. Appl Intell 25, 269–292 (2006). https://doi.org/10.1007/s10489-006-0107-y

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