Qualitative modelling via constraint programming
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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.
KeywordsConstraint programming Qualitative models
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- 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).Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.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.Google Scholar
- 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.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).Google Scholar
- 12.Clancy, D. (1998). Qualitative simulation as a temporally-extended constraint satisfaction problem. Proceedings AAAI, 98.Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- 16.Distler, A., & Kelsey, T. (2013). The semigroups of order 9 and their automorphism groups. Semigroup Forum, 1–20.Google Scholar
- 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
- 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.Google Scholar
- 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).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.Google Scholar
- 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.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).Google Scholar
- 32.Laburthe, F. Choco: a constraint programming kernel for solving combinatorial optimization problems, http://choco.sourceforge.net/.
- 34.Menzies, T., Compton, P., Feldman, B., Toth, T. (1992). Qualitative compartmental modelling. AAAI Technical Report SS-92-02.Google Scholar
- 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).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).Google Scholar
- 40.Robertson, D., Bundy, A., Meutzelfeldt, R., Haggith, M., Uschold, M. (1991). Eco-logic: logic-based approaches to ecological modelling. MIT Press.Google Scholar