Rule-Based Languages for Neurosymbolic AI
cs.AI, cs.LO
Submitted: 2026-10-05
Updated: 2026-10-05
Comments: To appear in the Proceedings of Rules and Reasoning - 10th International Joint Conference, RuleML+RR 2026, Vilnius, Lithuania
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Logic programming is increasingly used as the symbolic component of neurosymbolic AI systems.
Abstract
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.
Sources
- The DeepLog Neurosymbolic Machine
- Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
- ESCA: Contextualizing Embodied Agents via Scene-Graph Generation
- DeepLog: A Software Framework for Modular Neurosymbolic AI
- Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
- On Improving Neurosymbolic Learning by Exploiting the Representation Space
- Defining neurosymbolic AI
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