A Survey on Neural-symbolic Learning Systems

2021/11/10 by Dongran Yu, Yu, Dongran, Bo Yang +6 · 11 citations
Computer Science · #Advanced Graph Neural Networks #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2111.08164

openalex publication_date 2021/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they have been found to lack effective reasoning and cognitive ability. On the other hand, symbolic systems exhibit exceptional cognitive intelligence but suffer from poor learning capabilities when compared to neural systems. Recognizing the advantages and disadvantages of both methodologies, an ideal solution emerges: combining neural systems and symbolic systems to create neural-symbolic learning systems that possess powerful perception and cognition. The purpose of this paper is to survey the advancements in neural-symbolic learning systems from four distinct perspectives: challenges, methods, applications, and future directions. By doing so, this research aims to propel this emerging field forward, offering researchers a comprehensive and holistic overview. This overview will not only highlight the current state-of-the-art but also identify promising avenues for future research.

Citations

• Harnessing Deep Neural Networks with Logic Rules
• Semi-Supervised Classification with Graph Convolutional Networks
• Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
• Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
• Logical Rule Induction and Theory Learning Using Neural Theorem Proving
• Neural Markov Logic Networks
• Inductive Logic Programming via Differentiable Deep Neural Logic Networks
• The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
• Efficient Probabilistic Logic Reasoning with Graph Neural Networks
• Generalizing from a Few Examples

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• Physics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience
• Neural Logic Networks for Interpretable Classification
• When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
• Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey
• Enhancing Symbolic Machine Learning by Subsymbolic Representations
• Building Trustworthy AI by Addressing its 16+2 Desiderata with Goal-Directed Commonsense Reasoning
• SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
• Foundation Models for Tabular Data within Systemic Contexts Need Grounding

Related

• A survey on neural-symbolic learning systems
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