Bibliography

1

B.R. Binger and E. Hoffman.
Microeconomics with Calculus.
Longman, 1998.

2

S. Bistarelli, U. Montanari, and F. Rossi.
Semiring-based constraint satisfaction and optimization.
Journal of the ACM, 44(2):201-236, 1997.

3

D. Bobrow, B. Falkenhainer, A. Farquhar, R. Fikes, K.D. Forbus, T.R. Gruber, Y. Iwasaki, and B.J. Kuipers.
A compositional modeling language.
In Proceedings of the 10th International Workshop on Qualitative Reasoning about Physical Systems, pages 12-21, 1996.

4

E. Bradley, M. Easley, and R. Stolle.
Reasoning about nonlinear system identification.
Artificial Intelligence, 133:139-188, 2001.

5

P. Dague.
Numeric reasoning with relative orders of magnitude.
In Proceedings of the National Conference on Artificial Intelligence, pages 541-547, 1993.

6

P. Dague.
Symbolic reasoning with relative orders of magnitude.
In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1509-1514, 1993.

7

J. de Kleer.
An assumption-based TMS.
Artificial Intelligence, 28:127-162, 1986.

8

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

9

M. Easley and E. Bradley.
Generalized physical networks for automated model building.
In Proceedings of the 16th International Joint Conference on Artificial Intelligence, pages 1047-1053, 1999.

10

B. Falkenhainer and K. Forbus.
Compositional modeling: finding the right model for the job.
Artificial Intelligence, 51:95-143, 1991.

11

A. Ford.
Modeling the Environment - An Introduction to System Dynamics Modeling of Environmental Systems.
Island Press, 1999.

12

J.W. Forrester.
Principles of Systems.
Wright-Allen Press, Cambridge, MA, USA, 1968.

13

P.E. Hart, N.I. Nilsson, and B. Raphael.
A formal basis for the heuristic determination of minimal cost paths.
IEEE Transactions on Systems, Science and Cybernetics, SSC-4(2):100-107, 1968.

14

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

15

U. Heller and P. Struss.
Transformation of qualitative dynamic models - application in hydro-ecology.
In L. Hotz, P. Struss, and T. Guckenbienl, editors, Intelligent Diagnosis in Industrial Applications, pages 95-106. Shaker Verlag, 2001.

16

C.S. Holling.
Some characteristics of simple types of predation and parasitism.
Canadian Entomologist, 91:385-398, 1959.

17

D.C. Karnopp, D.L. Margolis, and R.C. Rosenberg.
System Dynamics: A United Approach.
John Wiley & Sons, Inc, second edition edition, 1990.

18

J. Keppens.
Compositional Ecological Modelling via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences.
PhD thesis, The University of Edinburgh, 2002.

19

J. Keppens and Q. Shen.
Disaggregation in compositional modelling of ecological systems via dynamic constraint satisfaction.
In Proceedings of the 15th International Workshop on Qualitative Reasoning about Physical Systems, pages 21-28, 2001.

20

J. Keppens and Q. Shen.
On compositional modelling.
Knowledge Engineering Review, 16(2):157-200, 2001.

21

J. Keppens and Q. Shen.
On supporting dynamic constraint satisfaction with order of magnitude preferences.
In Proceedings of the 16th International Workshop on Qualitative Reasoning about Physical Systems, pages 75-82, 2002.

22

P. Langley, J. Sanchez, L. Todorovski, and S. Dzeroski.
Inducing process models from continuous data.
In Proceedings of the 19th International Conference on Machine Learning, pages 347-354, 2002.

23

C.J. Legg, R.I. Muetzelfeldt, and D.N. Heathfield.
Modelling vegetation dynamics in mediterranean ecosystems: Issues of scale.
In Proceedings of the 39th Symposium of the International Association for Vegetation Science, 1995.

24

A.Y. Levy, Y. Iwasaki, and R. Fikes.
Automated model selection for simulation based on relevance reasoning.
Artificial Intelligence, 96:351-394, 1997.

25

A.J. Lotka.
Elements of physical biology.
Williams & Wilkins Co., Baltimore, 1925.

26

T.R. Malthus.
An essay on the principle of population.
Printed for J. Johnson in St. Paul's Church Yard, London, England, 1798.

27

I. Miguel and Q. Shen.
Hard, flexible and dynamic constraint satisfaction.
Knowledge Engineering Review, 14(3):199-220, 1999.

28

I. Miguel and Q. Shen.
Solution techniques for constraint satisfaction problems: Advanced approaches.
Artificial Intelligence Review, 15(4):269-293, 2001.

29

I. Miguel and Q. Shen.
Solution techniques for constraint satisfaction problems: Foundations.
Artificial Intelligence Review, 15(4):243-267, 2001.

30

S. Minton, M.D. Johnston, A.B. Philips, and P. Laird.
Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems.
Artificial Intelligence, 58:161-205, 1992.

31

S. Mittal and B. Falkenhainer.
Dynamic constraint satisfaction problems.
In Proceedings of the 8th National Conference on Artificial Intelligence, pages 25-32, 1990.

32

P.P. Nayak and L. Joskowicz.
Efficient compositional modeling for generating causal explanations.
Artificial Intelligence, 83:193-227, 1996.

33

A.J. Nicholson and V.A. Bailey.
The balance of animal populations.
Proceedings of the Zoological Society of London, 1:551-598, 1935.

34

B. Raphael.
A* algorithm.
In Shapiro, S.C., editor, Encyclopedia of Artificial Intelligence, volume 1, pages 1-3. John Wiley & Sons, 1990.

35

J. Rickel and B. Porter.
Automated modeling of complex systems to answer prediction questions.
Artificial Intelligence, 93:201-260, 1997.

36

D.J. Rogers.
Random search and insect population models.
Journal of Animal Ecology, 41:369-383, 1972.

37

T. Schiex, H. Fargier, and G. Verfaillie.
Valued constraint satisfaction problems: Hard and easy problems.
In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 631-637, 1995.

38

W.R. Thompson.
On the relative value of parasites and predators in the biological control of insect pests.
Bull. Etnomol. Res., 19:343-350, 1929.

39

L. Todorovski and S. Dzeroski.
Declarative bias in equation discovery.
In Proceedings of the 14th International Conference on Machine Learning, pages 432-439, 1997.

40

L. Todorovski and S. Dzeroski.
Using domain knowledge on population dynamics modeling for equation discovery.
In Proceedings of the 12th European Conference on Machine Learning, pages 478-490, 2001.

41

E. Tsang.
Foundations of Constraint Satisfaction.
Academic Press, London and San Diego, 1993.

42

G. Verfaillie and T. Schiex.
Solution reuse in dynamic constraint satisfaction problems.
In Proceedings of the 12th National Conference on Artificial Intelligence, pages 307-312, 1994.

43

P. Verhulst.
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, 1838.

44

V. Volterra.
Fluctuations in the abundance of a species considered mathematically.
Nature, 118:558-560, 1926.