Towards Portfolios of Streamlined Constraint Models: A Case Study with the Balanced Academic Curriculum Problem

TL;DR

This paper investigates automatic streamliner constraints in constraint models, emphasizing model selection's impact and proposing a best-first search method to optimize model portfolios for the Balanced Academic Curriculum Problem.

Contribution

It introduces a novel best-first search approach to generate Pareto optimal streamliner-model portfolios, enhancing automated constraint model refinement.

Findings

Model selection significantly affects streamliner effectiveness.

The proposed method efficiently identifies optimal model portfolios.

Performance variability is effectively managed through racing techniques.

Abstract

Augmenting a base constraint model with additional constraints can strengthen the inferences made by a solver and therefore reduce search effort. We focus on the automatic addition of streamliner constraints, derived from the types present in an abstract Essence specification of a problem class of interest, which trade completeness for potentially very significant reduction in search. The refinement of streamlined Essence specifications into constraint models suitable for input to constraint solvers gives rise to a large number of modelling choices in addition to those required for the base Essence specification. Previous automated streamlining approaches have been limited in evaluating only a single default model for each streamlined specification. In this paper we explore the effect of model selection in the context of streamlined specifications. We propose a new best-first search method that generates a portfolio of Pareto Optimal streamliner-model combinations by evaluating for each streamliner a portfolio of models to search and explore the variability in performance and find the optimal model. Various forms of racing are utilised to constrain the computational cost of training.