# Neural Guided Constraint Logic Programming for Program Synthesis

arXiv: https://arxiv.org/abs/1809.02840 · HTML: https://mlsanity.com/paper/1809.02840/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey](https://mlsanity.com/paper/2108.11451/) (2021)
- [Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing](https://mlsanity.com/paper/2210.15889/) (2022)
- [Program Synthesis Guided Reinforcement Learning for Partially Observed Environments](https://mlsanity.com/paper/2102.11137/) (2021)
- [MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation](https://mlsanity.com/paper/2210.03003/) (2022)
- [Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network](https://mlsanity.com/paper/2402.02627/) (2024)
- [COOL: A Constraint Object-Oriented Logic Programming Language and its Neural-Symbolic Compilation System](https://mlsanity.com/paper/2311.03753/) (2023)

## Abstract (arXiv)

Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive constraints imposed by the provided examples. We explore Recurrent Neural Network and Graph Neural Network models. We contribute a modified miniKanren, drivable by an external agent, available at https://github.com/xuexue/neuralkanren. We show that our neural-guided approach using constraints can synthesize programs faster in many cases, and importantly, can generalize to larger problems.