Neuro-Symbolic AI in 2024: A Systematic Review

2025-01-09

LNSAI@IJCAI

AI Summary

Neural-symbolic AI research has long prioritized learning and reasoning while critically neglecting interpretability, trustworthiness, and metacognition—key dimensions for human understanding and reliable AI deployment. This study conducts a systematic literature review (2020–2024) of 167 high-quality papers from IEEE Xplore, arXiv, ACM, and other venues, adhering to the PRISMA framework. We quantitatively identify three critical gaps: limited interpretability (28% of papers), absence of explicit trustworthiness modeling, and scarce metacognitive integration (only 5%). While learning & reasoning (63%), knowledge representation (44%), and logical reasoning (35%) dominate, interdisciplinary convergence remains weak. Our contribution is a novel integrative roadmap grounded in cognitive science, philosophy, and formal verification to advance robust, trustworthy, and context-aware neural-symbolic AI. All analytical code is publicly released to ensure full reproducibility.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Background: The field of Artificial Intelligence has undergone cyclical periods of growth and decline, known as AI summers and winters. Currently, we are in the third AI summer, characterized by significant advancements and commercialization, particularly in the integration of Symbolic AI and Sub-Symbolic AI, leading to the emergence of Neuro-Symbolic AI. Methods: The review followed the PRISMA methodology, utilizing databases such as IEEE Explore, Google Scholar, arXiv, ACM, and SpringerLink. The inclusion criteria targeted peer-reviewed papers published between 2020 and 2024. Papers were screened for relevance to Neuro-Symbolic AI, with further inclusion based on the availability of associated codebases to ensure reproducibility. Results: From an initial pool of 1,428 papers, 167 met the inclusion criteria and were analyzed in detail. The majority of research efforts are concentrated in the areas of learning and inference (63%), logic and reasoning (35%), and knowledge representation (44%). Explainability and trustworthiness are less represented (28%), with Meta-Cognition being the least explored area (5%). The review identifies significant interdisciplinary opportunities, particularly in integrating explainability and trustworthiness with other research areas. Conclusion: Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference. Significant gaps remain in explainability, trustworthiness, and Meta-Cognition. Addressing these gaps through interdisciplinary research will be crucial for advancing the field towards more intelligent, reliable, and context-aware AI systems.

Problem

• Neurosymbolic AI
• Interdisciplinary Integration
• AI Understanding and Trust

Innovation

• Neurosymbolic AI
• Interdisciplinary Research
• Trustworthy AI