Automatic Feature Learning for Essence: a Case Study on Car Sequencing

TL;DR

This paper introduces a method to automatically learn features from high-level problem descriptions using language models, improving the selection of constraint models and solvers in car sequencing problems.

Contribution

It presents a novel approach to automatically extract instance features from high-level models with language models, aiding solver selection.

Findings

Improved solver selection accuracy for car sequencing instances.

Demonstrated effectiveness of language model-based feature learning.

Enhanced problem-solving efficiency through automatic feature extraction.

Abstract

Constraint modelling languages such as Essence offer a means to describe combinatorial problems at a high-level, i.e., without committing to detailed modelling decisions for a particular solver or solving paradigm. Given a problem description written in Essence, there are multiple ways to translate it to a low-level constraint model. Choosing the right combination of a low-level constraint model and a target constraint solver can have significant impact on the effectiveness of the solving process. Furthermore, the choice of the best combination of constraint model and solver can be instance-dependent, i.e., there may not exist a single combination that works best for all instances of the same problem. In this paper, we consider the task of building machine learning models to automatically select the best combination for a problem instance. A critical part of the learning process is to define instance features, which serve as input to the selection model. Our contribution is automatic learning of instance features directly from the high-level representation of a problem instance using a language model. We evaluate the performance of our approach using the Essence modelling language with a case study involving the car sequencing problem.