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Qualitative modelling via constraint programming

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Abstract

Qualitative modelling is a technique integrating the fields of theoretical computer science, artificial intelligence and the physical and biological sciences. The aim is to be able to model the behaviour of systems without estimating parameter values and fixing the exact quantitative dynamics. Traditional applications are the study of the dynamics of physical and biological systems at a higher level of abstraction than that obtained by estimation of numerical parameter values for a fixed quantitative model. Qualitative modelling has been studied and implemented to varying degrees of sophistication in Petri nets, process calculi and constraint programming. In this paper we reflect on the strengths and weaknesses of existing frameworks, we demonstrate how recent advances in constraint programming can be leveraged to produce high quality qualitative models, and we describe the advances in theory and technology that would be needed to make constraint programming the best option for scientific investigation in the broadest sense.

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References

  1. Aggoun, A., Chan, D., Dufresne, P., Falvey, E., Grant, H., Harvey, W., Herold, A., Macartney, G., Meier, M., Miller, D., Mudambi, S., Novello, S., Perez, B., van Rossum, E., Schimpf, J., Shen, K., Tsahageas, P.A., de Villeneuve, D.H. (2006). Eclipse user manual release 5.10, http://eclipse-clp.org/.

  2. Balasubramaniam, D., de Silva, L., Jefferson, C., Kotthoff, L., Miguel, I., Nightingale, P. (2011). Dominion: an architecture-driven approach to generating efficient constraint solvers. In 9th Working IEEE/IFIP conference on software architecture (WICSA) (pp. 228–231).

  3. Balasubramaniam, D., Jefferson, C., Kotthoff, L., Miguel, I., Nightingale, P. (2012). An automated approach to generating efficient constraint solvers. In 34th international conference on software engineering.

  4. Beldiceanu, N., & Simonis, H. A model seeker: extracting global constraint models from positive examples. In M. Milano (Ed.), Principles and practice of constraint programming - 18th international conference, CP 2012, Quebec City, QC, Canada, October 8–12, 2012. Proceedings, lecture notes in computer science (vol. 7514, pp. 141–157). Springer.

  5. Bockmayr, A., & Courtois, A. (2002). Using hybrid concurrent constraint programming to model dynamic biological systems. In 18th international conference on logic programming (pp. 85–99). Springer.

  6. Bristol-Gould, S.K., Kreeger, P.K., Selkirk, C.G., Kilen, S.M., Mayo, K.E., Shea, L.D., Woodruff, T.K. (2006). Fate of the initial follicle pool: empirical and mathematical evidence supporting its sufficiency for adult fertility. Developmental biology, 298(1), 149–54.

    Article  Google Scholar 

  7. Calder, M., & Hillston, J. (2009). Process algebra modelling styles for biomolecular processes. In C. Priami, R.J. Back, I. Petre (Eds), Transactions on computational systems biology XI (pp. 1–25). Berlin, Heidelberg: Springer-Verlag.

  8. Calzone, L., Chabrier-Rivier, N., Fages, F., Soliman, S. (2006). Machine learning biochemical networks from temporal logic properties. The Computer System Biology, 68–94.

  9. Calzone, L., Fages, F., Soliman, S. (2006). BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics (Oxford, England), 22(14), 1805–7.

    Article  Google Scholar 

  10. Cimatti, A., Micheli, A., Roveri, M. Solving temporal problems using smt: Strong controllability. In M. Milano (Ed.), Principles and practice of constraint programming - 18th international conference, CP 2012, Quebec City, QC, Canada, October 8–12, 2012. Proceedings, lecture notes in computer science (vol. 7514, pp. 248–264).

  11. Ciocchetta, F., & Hillston, J. (2009). Bio-pepa: a framework for the modelling and analysis of biological systems. Theoretical Computer Science, 410(33-34), 3065–3084.

    Article  MATH  MathSciNet  Google Scholar 

  12. Clancy, D. (1998). Qualitative simulation as a temporally-extended constraint satisfaction problem. Proceedings AAAI, 98.

  13. Degasperi, A., & Calder, M. (2009). On the formalisation of gradient diffusion models of biological systems. In Proceedings 8th workshop on process algebra and stochastically timed activities (pp. 139–144).

  14. Distler, A., Kelsey, T., Kotthoff, L., Jefferson, C. (2012). The semigroups of order 10. In CP. lecture notes in computer science (vol. 7514, pp. 883–899). Springer.

  15. Distler, A., & Kelsey, T. (2009). The monoids of orders eight, nine & ten. Annals of Mathematics and Artificial Intelligence, 56(1), 3–21.

    Article  MATH  MathSciNet  Google Scholar 

  16. Distler, A., & Kelsey, T. (2013). The semigroups of order 9 and their automorphism groups. Semigroup Forum, 1–20.

  17. Escrig, M.T., Cabedo, L.M., Pacheco, J., Toledo, F. (2002). Several models on qualitative motion as instances of the CSP. Revista Iberoamericana de Inteligencia Artificial, 6(17), 55–71.

    Google Scholar 

  18. Faddy, M.J., & Gosden, R.G. (1995). A mathematical model of follicle dynamics in the human ovary. Human reproduction (Oxford, England), 10(4), 770–5.

    Google Scholar 

  19. Fleming, R., Kelsey, T.W., Anderson, R.A., Wallace, W.H., Nelson, S.M. (2012). Interpreting human follicular recruitment and antimullerian hormone concentrations throughout life. Fertility and Sterility, 98(5), 1097–1102.

    Article  Google Scholar 

  20. Frisch, A.M., Harvey, W., Jefferson, C., Martínez-Hernández, B., Miguel, I. (2008). Essence: a constraint language for specifying combinatorial problems. Constraints, 13(3), 268–306.

    Article  MATH  MathSciNet  Google Scholar 

  21. Gent, I.P., Jefferson, C., Miguel, I. (2006). Minion: a fast scalable constraint solver. In G. Brewka, S. Coradeschi, A. Perini, P. Traverso (Eds.), The European conference on artificial intelligence 2006 (ECAI 06) (pp. 98-102). IOS Press.

  22. Gent, I.P., Jefferson, C.A., Miguel, I. (2006). MINION: a fast scalable constraint solver. In Proceedings of the 17th european conference on artificial intelligence (pp. 98–102).

  23. Haefner, J. (2005). Modeling biological systems. New York: Springer-Verlag.

    MATH  Google Scholar 

  24. Hentenryck, P.V., Hentenryck, P.V., Michel, L., Michel, L. (1997). Newton: constraint programming over nonlinear real constraints. In Science of computer programming (pp. 1–2). Numerica: MIT Press.

  25. Hoda, S., van Hoeve, W.J., Hooker, J.N. (2010). A systematic approach to MDD-based constraint programming. In D. Cohen (Ed.), CP. Lecture notes in computer science (vol. 6308, pp. 266–280). Springer.

  26. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T. (2009). Paramils: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research, 36(1), 267–306.

    MATH  Google Scholar 

  27. Jefferson, C., Moore, N., Nightingale, P., Petrie, K.E. (2010). Implementing logical connectives in constraint programming. Artificial Intelligence, 174, 1407–1429.

    Article  MATH  MathSciNet  Google Scholar 

  28. Kelsey, T.W., Anderson, R.A., Wright, P., Nelson, S.M., Wallace, W.H.B. (2011). Data-driven assessment of the human ovarian reserve. Molecular human reproduction, 18(2), 79–87.

    Article  Google Scholar 

  29. Kelsey, T., & Linton, S. (2012). Qualitative models of cell dynamics as constraint satisfaction problems. In R. Backhoven, S. Will (Eds.), Proceedings of the workshop on constraint based methods for bioinformatics (WCB12) (pp. 16–22).

  30. Ko, K.I. (1984). On the computational complexity of ordinary differential equations. Information and Control, 58(1-3), 157–194.

    Article  Google Scholar 

  31. Kuipers, B. (1993). Reasoning with qualitative models. Artificial Intelligence, 59, 125–132.

    Article  Google Scholar 

  32. Laburthe, F. Choco: a constraint programming kernel for solving combinatorial optimization problems, http://choco.sourceforge.net/.

  33. Menzies, T., & Compton, P. (1997). Applications of abduction: hypothesis testing of neuroendocrinological qualitative compartmental models. Artificial Intelligence in Medicine, 10(2), 145–75.

    Article  Google Scholar 

  34. Menzies, T., Compton, P., Feldman, B., Toth, T. (1992). Qualitative compartmental modelling. AAAI Technical Report SS-92-02.

  35. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G. (2007). Minizinc: towards a standard cp modelling language. In Proceedings of 13th international conference on principles and practice of constraint programming (pp. 529–543).

  36. Radke-Sharpe, N., & White, K. (1998). The role of qualitative knowledge in the formulation of compartmental models. IEEE Transactions on Systems, Man and Cybernetics Part C (Applications and Reviews), 28(2), 272–275.

    Article  Google Scholar 

  37. Rendl, A., Miguel, I., Gent, I.P., Jefferson, C. (2009). Automatically enhancing constraint model instances during tailoring. In Proceedings of 8th symposium on abstraction, reformulation, and approximation (SARA).

  38. Rice, J.R. (1976). The algorithm selection problem. Advances in Computers, 15, 65–118.

    Article  Google Scholar 

  39. Rizk, A., Batt, G., Fages, F., Soliman, S. (2011). Continuous valuations of temporal logic specifications with applications to parameter optimization and robustness measures. Theoretical Computer Science, 412(26), 2827–2839.

    Article  MATH  MathSciNet  Google Scholar 

  40. Robertson, D., Bundy, A., Meutzelfeldt, R., Haggith, M., Uschold, M. (1991). Eco-logic: logic-based approaches to ecological modelling. MIT Press.

  41. Tsoukias, N.M. (2008). Nitric oxide bioavailability in the microcirculation: insights from mathematical models. Microcirculation (New York, N.Y. : 1994), 15(8), 813–34.

    Article  Google Scholar 

  42. Wallace, W.H.B., & Kelsey, T.W. (2010). Human ovarian reserve from conception to the menopause. PloS one, 5(1), e8772.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, University of St Andrews, KY16 9SX, St Andrews, UK

    Thomas W. Kelsey, Christopher A. Jefferson, Stephen A. Linton, Ian Miguel, Peter Nightingale & Ian P. Gent

  2. INSIGHT Centre for Data Analytics, University College Cork, Cork, Ireland

    Lars Kotthoff

Authors
  1. Thomas W. Kelsey
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  2. Lars Kotthoff
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  3. Christopher A. Jefferson
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  4. Stephen A. Linton
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  5. Ian Miguel
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  6. Peter Nightingale
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  7. Ian P. Gent
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Correspondence to Thomas W. Kelsey.

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Kelsey, T.W., Kotthoff, L., Jefferson, C.A. et al. Qualitative modelling via constraint programming. Constraints 19, 163–173 (2014). https://doi.org/10.1007/s10601-014-9158-6

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  • DOI: https://doi.org/10.1007/s10601-014-9158-6

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