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Neuro-Symbolic Artificial Intelligence: Current Trends

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arxiv 2105.05330 v2 pith:BODP6LDH submitted 2021-05-11 cs.AI cs.LG

Md Kamruzzaman Sarker , Lu Zhou , Aaron Eberhart , Pascal Hitzler This is my paper · ORCID
classification cs.AIcs.LG
keywords artificialarticlecurrentintelligencemethodsneuro-symbolictrendscategorizing
verification ladder T0 review T1 audit T2 compute T3 formal
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Neuro-Symbolic Artificial Intelligence -- the combination of symbolic methods with methods that are based on artificial neural networks -- has a long-standing history. In this article, we provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks

    cs.AI 2026-07 conditional novelty 5.5 of 10

    Decoupling Swin-based multilabel CODE prediction from a fixed 19-rule fuzzy DT reasoner improves imbalanced sewer severity metrics by roughly 12–23% over image-only classification while exposing rule-level evidence.

  2. Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions

    cs.LO 2026-02 unverdicted novelty 4.0 of 10

    AGI robots learn and deduce using Belnap's 4-valued bilattice and Closed Knowledge Assumption to expand knowledge while supporting inconsistencies and providing logical security.

  3. On LLM-generated Logic Programs and their Inference Execution Methods

    cs.AI 2025-02 conditional novelty 4.0 of 10

    The authors generate logic programs from LLM dialog threads and execute them with a GPU-based minimal model solver and soft-unification retrieval.

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