# Rule-Based Languages for Neurosymbolic AI

arXiv: https://arxiv.org/abs/2610.07313 · HTML: https://mlsanity.com/paper/2610.07313/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 101 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Builds on (most cited first, 7 of 7)

- [Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning](https://mlsanity.com/paper/2410.22077/) (2024)
- [Defining neurosymbolic AI](https://mlsanity.com/paper/2507.11127/) (2025)
- [Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts](https://mlsanity.com/paper/2510.14538/) (2025)
- [ESCA: Contextualizing Embodied Agents via Scene-Graph Generation](https://mlsanity.com/paper/2510.15963/) (2025)
- [The DeepLog Neurosymbolic Machine](https://mlsanity.com/paper/2508.13697/) (2025)
- [DeepLog: A Software Framework for Modular Neurosymbolic AI](https://mlsanity.com/paper/2605.10279/) (2026)
- [On Improving Neurosymbolic Learning by Exploiting the Representation Space](https://mlsanity.com/paper/2602.07973/) (2026)

## Abstract (arXiv)

Logic programming is increasingly used as the symbolic component of neurosymbolic AI systems. We survey the main rule-based languages in this setting, namely Datalog, answer set, and probabilistic logic programs, along four axes: semantics, expressiveness, neural integration, and evaluation mechanism. We analyse over 50 recent systems and applications, comparing formalism usage across four research areas: databases and programming languages, machine learning, vision, and robotics. We provide a decision matrix mapping application scenarios to required features and close by outlining open problems.