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Constraints

, Volume 19, Issue 2, pp 163–173 | Cite as

Qualitative modelling via constraint programming

  • Thomas W. Kelsey
  • Lars Kotthoff
  • Christopher A. Jefferson
  • Stephen A. Linton
  • Ian Miguel
  • Peter Nightingale
  • Ian P. Gent
Article

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.

Keywords

Constraint programming Qualitative models 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Thomas W. Kelsey
    • 1
  • Lars Kotthoff
    • 2
  • Christopher A. Jefferson
    • 1
  • Stephen A. Linton
    • 1
  • Ian Miguel
    • 1
  • Peter Nightingale
    • 1
  • Ian P. Gent
    • 1
  1. 1.School of Computer ScienceUniversity of St AndrewsSt AndrewsUK
  2. 2.INSIGHT Centre for Data AnalyticsUniversity College CorkCorkIreland

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