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Qualitative modelling via constraint programming

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Author(s)

  • Thomas William Kelsey
  • Lars Kotthoff
  • Christopher Anthony Jefferson
  • Stephen Alexander Linton
  • Ian James Miguel
  • Peter William Nightingale
  • Ian Philip Gent

Department/unit(s)

Publication details

JournalJournal of Constraints
DateAccepted/In press - 16 Jan 2014
DateE-pub ahead of print - 12 Feb 2014
DatePublished (current) - 1 Apr 2014
Issue number2
Volume19
Pages (from-to)163-173
Early online date12/02/14
Original languageEnglish

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.

Bibliographical note

This work was supported by United Kingdom EPSRC grant EP/H004092/1. LK was supported by EU FP7 grants 284715 (ICON) and 288147 (ePolicy).

    Research areas

  • Constraint programming, Qualitative models

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