Rule-Based Languages for Neurosymbolic AI

Stefania Dumbrava, Efthymia Tsamoura
10/5/2026
cs.AIcs.LO

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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.

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Cite this paper

@article{dumbrava2026rulebased,
  title  = {Rule-Based Languages for Neurosymbolic AI},
  author = {Stefania Dumbrava and Efthymia Tsamoura},
  year   = {2026},
  eprint = {2610.07313},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.07313v1}
}

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