Rule-Based Languages for Neurosymbolic AI

2026-10-05

AI Summary

This study addresses the lack of systematic guidance for selecting logic programming languages in neuro-symbolic AI. It surveys rule-based languages—including Datalog, Answer Set Programming, and probabilistic logic programs—across four dimensions such as semantics and expressiveness, while analyzing over fifty systems. By constructing application-feature mapping and decision matrices, this work compares formal methodological differences across domains and examines integration techniques with neural networks. Ultimately, it establishes a selection guideline for rule-based languages alongside a future research roadmap, providing theoretical support for system design and the resolution of open problems in this field.

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.

Problem

• Neurosymbolic AI
• Rule-based languages
• Logic programming
• Survey

Innovation

• Neurosymbolic AI
• Rule-based languages
• Logic programming
• Decision matrix
• Probabilistic logic programs