English

Towards Exploratory Reformulation of Constraint Models

Artificial Intelligence 2023-11-21 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2311.11868,
  title  = {Towards Exploratory Reformulation of Constraint Models},
  author = {Ian Miguel and András Z. Salamon and Christopher Stone},
  journal= {arXiv preprint arXiv:2311.11868},
  year   = {2023}
}

Comments

13 pages, 6 figures

R2 v1 2026-06-28T13:26:12.160Z