Granularity and disaggregation in compositional modelling with applications to ecological systems

Applied Intelligence

Volumn 25, Issue 3, 2006, Pages 269-292

  Keppens, Jeroen ^a   Shen, Qiang ^b  

^a KING'S COLLEGE LONDON   (United Kingdom)

^b ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

Compositional modelling; Disaggregation; Ecological systems; Granularity; Model based reasoning

Indexed keywords

COMPUTER SIMULATION; ECONOMIC AND SOCIAL EFFECTS; GEOLOGICAL REPOSITORIES; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS;

COMPOSITIONAL MODELING (CM); DISAGGREGATION; ECOLOGICAL SYSTEMS; GRANULARITY; MODEL BASED REASONING;

ECOSYSTEMS;

EID: 33750727364     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-006-0107-y     Document Type: Article

References (37)

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
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
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
6. de Kleer J (1986) An assumption-based TMS. Artif Intell 28:127-162
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
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
13. Gottschalk P (1983) A system dynamics model for long range planning in a railroad. Eur J Oper Res 14(2): 156-162
14. Hamscher W, Console L, de Kleer J (eds) (1992) Readings in model-based diagnosis. Morgan-Kaufmann, San Mateo
15. Hastings A (1997) Population biology. Concepts and models. Springer-Verlag, New York
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
19. Ittig PT (1976) A model for planning ambulatory health services. Manag Sci 24(10):1001-1010
20. Keppens J, Shen Q (2001) On compositional modelling. Knowl Eng Rev 16(2):157-200
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
22. Kuipers BJ (1993) Reasoning with qualitative models. Artif Intell 59:125-132
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
25. Levy AY, Iwasaki Y, Fikes R (1997) Automated model selection for simulation based on relevance reasoning. Artif Intell 96:351-394
26. Malthus TR (1798) An essay on the principle of population. Printed for J. Johnson in St. Paul's Church Yard, London, England
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
29. Rickel J, Porter B (1997) Automated modeling of complex systems to answer prediction questions. Artif Intell 93:201-260
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
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
36. Verhulst P ( 1838) Recherches mathématiques sur la loi d'accroissement de la population. Nouveaux mémoires de l'académie royale des sciences et belles-lettres de Bruxelles 18:1-38
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