# Declarative Learning-Based Programming as an Interface to AI Systems

arXiv: https://arxiv.org/abs/1906.07809 · HTML: https://mlsanity.com/paper/1906.07809/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

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

- [Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language](https://mlsanity.com/paper/2407.20513/) (2024)
- [Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis](https://mlsanity.com/paper/2509.07122/) (2025)
- [Intent-Driven UAM Rescheduling](https://mlsanity.com/paper/2512.15462/) (2025)

## Abstract (arXiv)

Data-driven approaches are becoming more common as problem-solving techniques in many areas of research and industry. In most cases, machine learning models are the key component of these solutions, but a solution involves multiple such models, along with significant levels of reasoning with the models' output and input. Current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas and models on real-world data in the context of the overall AI system. We review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques and the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.