The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Neurosymbolic AI: the 3rd wave
Open accessAbstract
Current advances in Artificial Intelligence (AI) and Machine Learning have achieved unprecedented impact across research communities and industry. Nevertheless, concerns around trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neurosymbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability by offering symbolic representations for neural models. In this paper, we relate recent and early research in neurosymbolic AI with the objective of identifying the most important ingredients of neurosymbolic AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. Finally, this review identifies promising directions and challenges for the next decade of AI research from the perspective of neurosymbolic computing, commonsense reasoning and causal explanation.
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D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
Vowels, M. J., Camgoz, N. C., & Bowden, R. (2023) · ACM Computing Surveys · doi:10.1145/3527154 · Cited by 237
Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications
Pati, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3585609 · Cited by 100
Solanke, A. A. (2022) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2022.301403 · Cited by 52
Towards Cognitive AI Systems: Workload and Characterization of Neuro-Symbolic AI
Wan, Z., Liu, C.-K., Yang, H., Raj, R., Li, C., You, H., Fu, Y., Wan, C., Samajdar, A., Lin, Y. C., et al. (2024) · 2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass61541.2024.00033 · Cited by 39
A Critical Review of Inductive Logic Programming Techniques for Explainable AI
Zhang, Z., Yilmaz, L., & Liu, B. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3246980 · Cited by 25
Metadata
- Authors
- Garcez, A. D., & Lamb, L. C.
- Venue
- Artificial Intelligence Review
- Published
- 2023-03-15
- Volume
- 56
- Issue
- 11
- Pages
- 12387-12406
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3113149630
- Cited by
- 503
- References
- 89
- FWCI
- 39.36
- Type
- journal article
- Language
- en
- Open access
- green
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