Qualitative Modelling via Constraint Programming: Past, Present and Future
This is an incremental review and perspective paper for researchers in AI and computational sciences, focusing on enhancing qualitative modeling techniques.
The paper reviews qualitative modeling approaches, highlighting how recent advances in constraint programming can improve model quality, and outlines future theoretical and technological needs to make it a superior option for scientific investigation.
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.