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

arXiv: https://arxiv.org/abs/2409.15158 · HTML: https://mlsanity.com/paper/2409.15158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Quantization Networks](https://mlsanity.com/paper/1911.09464/) (2019)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Holy Grail 2.0: From Natural Language to Constraint Models](https://mlsanity.com/paper/2308.01589/) (2023)
- [Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches](https://mlsanity.com/paper/2203.13392/) (2022)
- [An Enhanced Features Extractor for a Portfolio of Constraint Solvers](https://mlsanity.com/paper/1308.0227/) (2013)
- [sunny-as2: Enhancing SUNNY for Algorithm Selection](https://mlsanity.com/paper/2009.03107/) (2020)
- [Towards an Automatic Optimisation Model Generator Assisted with Generative Pre-trained Transformer](https://mlsanity.com/paper/2305.05811/) (2023)
- [Proteus: A Hierarchical Portfolio of Solvers and Transformations](https://mlsanity.com/paper/1306.5606/) (2013)
- [Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity](https://mlsanity.com/paper/2007.07803/) (2020)
- [A Framework for Generating Informative Benchmark Instances](https://mlsanity.com/paper/2205.14753/) (2022)
- [A portfolio-based analysis method for competition results](https://mlsanity.com/paper/2205.15414/) (2022)

## Abstract (arXiv)

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.