Towards Exploratory Reformulation of Constraint Models
This addresses the problem of inefficient constraint modeling for practitioners in constraint programming, but it is incremental as it builds on existing refinement-based approaches.
The paper tackles the challenge of selecting the best constraint model for a given problem by proposing a system that explores model spaces through reformulation, guided by performance on training instances, though no concrete results or numbers are provided as it is a position paper outlining a plan.
It is well established that formulating an effective constraint model of a problem of interest is crucial to the efficiency with which it can subsequently be solved. Following from the observation that it is difficult, if not impossible, to know a priori which of a set of candidate models will perform best in practice, we envisage a system that explores the space of models through a process of reformulation from an initial model, guided by performance on a set of training instances from the problem class under consideration. We plan to situate this system in a refinement-based approach, where a user writes a constraint specification describing a problem above the level of abstraction at which many modelling decisions are made. In this position paper we set out our plan for an exploratory reformulation system, and discuss progress made so far.