# The Inductive Constraint Programming Loop

arXiv: https://arxiv.org/abs/1510.03317 · HTML: https://mlsanity.com/paper/1510.03317/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Easy, adaptable and high-quality Modelling with domain-specific Constraint Patterns](https://mlsanity.com/paper/2206.02479/) (2022)
- [Integrating Symbolic Reasoning into Neural Generative Models for Design Generation](https://mlsanity.com/paper/2310.09383/) (2023)
- [Predictive Machine Learning of Objective Boundaries for Solving COPs](https://mlsanity.com/paper/2111.03160/) (2021)

## Abstract (arXiv)

Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.