Neuro-Symbolic Adaptation for Autonomous Systems

Alex Morgan

Abstract

This review examines neuro-symbolic adaptation for autonomous systems. The organizing question is when learned perception and symbolic structure can support transparent adaptation under novel conditions. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to assuming symbolic components guarantee faithful or correct reasoning. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in adaptive autonomous decision systems.

Keywords

neuro-symbolic AIautonomous systemsadaptationreasoningexplainability

References

Chandre, P., Mahalle, P., Shinde, G., Shendkar, B., & Kashid, S. (2025). Neuro-Symbolic AI: A Future of Tomorrow. ASEAN Journal on Science and Technology for Development, 42(2). https://doi.org/10.61931/2224-9028.1620 DOI
Chen, W., Ma, X., Wang, Z., Li, W., Fan, C., Zhang, J., Que, X., & Li, C. (2024). Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction. Earth Science Informatics, 17(3), 1819-1835. https://doi.org/10.1007/s12145-024-01278-7 DOI
Colelough, B. C., & Regli, W. (2025). Neuro-Symbolic AI in 2024: A Systematic Review. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.05435 DOI
Gaur, M., Gunaratna, K., Bhatt, S., & Sheth, A. (2022). Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI. IEEE Internet Computing, 26(4), 5-11. https://doi.org/10.1109/mic.2022.3179759 DOI
Hagos, D. H., & Rawat, D. B. (2024). Neuro-Symbolic AI for Military Applications. IEEE Transactions on Artificial Intelligence, 5(12), 6012-6026. https://doi.org/10.1109/tai.2024.3444746 DOI
Hamilton, K., Nayak, A., Božić, B., & Longo, L. (2022). Is neuro-symbolic AI meeting its promises in natural language processing? A structured review. Semantic Web, 15(4), 1265-1306. https://doi.org/10.3233/sw-223228 DOI
Li, Q., Liu, Y., Sun, S., Qin, Z., & Chu, F. (2024). Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI. Journal of Manufacturing Systems, 77, 652-661. https://doi.org/10.1016/j.jmsy.2024.10.007 DOI
Nawaz, U., Anees-ur-Rahaman, M., & Saeed, Z. (2025). A review of neuro-symbolic AI integrating reasoning and learning for advanced cognitive systems. Intelligent Systems with Applications, 26, 200541. https://doi.org/10.1016/j.iswa.2025.200541 DOI
Prenosil, G. A., Weitzel, T. K., Bello, S. C., Mingels, C., Manzini, G., Meier, L. P., Shi, K. Y., Rominger, A., & Afshar-Oromieh, A. (2025). Neuro-symbolic AI for auditable cognitive information extraction from medical reports. Communications Medicine, 5(1), 491. https://doi.org/10.1038/s43856-025-01194-x DOI
Susskind, Z., Arden, B., John, L. K., Stockton, P., & John, E. B. (2021). Neuro-Symbolic AI: An Emerging Class of AI Workloads and their\n Characterization. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2109.06133 DOI

Publication details

Journal: Advances in Adaptive Intelligence
Volume: 1 (2026)
Article number: aai20260003
License: CC BY 4.0

[1] Chandre, P., Mahalle, P., Shinde, G., Shendkar, B., & Kashid, S. (2025). Neuro-Symbolic AI: A Future of Tomorrow. ASEAN Journal on Science and Technology for Development, 42(2). https://doi.org/10.61931/2224-9028.1620 DOI

[2] Chen, W., Ma, X., Wang, Z., Li, W., Fan, C., Zhang, J., Que, X., & Li, C. (2024). Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction. Earth Science Informatics, 17(3), 1819-1835. https://doi.org/10.1007/s12145-024-01278-7 DOI

[3] Colelough, B. C., & Regli, W. (2025). Neuro-Symbolic AI in 2024: A Systematic Review. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2501.05435 DOI

[4] Gaur, M., Gunaratna, K., Bhatt, S., & Sheth, A. (2022). Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI. IEEE Internet Computing, 26(4), 5-11. https://doi.org/10.1109/mic.2022.3179759 DOI

[5] Hagos, D. H., & Rawat, D. B. (2024). Neuro-Symbolic AI for Military Applications. IEEE Transactions on Artificial Intelligence, 5(12), 6012-6026. https://doi.org/10.1109/tai.2024.3444746 DOI

[6] Hamilton, K., Nayak, A., Božić, B., & Longo, L. (2022). Is neuro-symbolic AI meeting its promises in natural language processing? A structured review. Semantic Web, 15(4), 1265-1306. https://doi.org/10.3233/sw-223228 DOI

[7] Li, Q., Liu, Y., Sun, S., Qin, Z., & Chu, F. (2024). Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI. Journal of Manufacturing Systems, 77, 652-661. https://doi.org/10.1016/j.jmsy.2024.10.007 DOI

[8] Nawaz, U., Anees-ur-Rahaman, M., & Saeed, Z. (2025). A review of neuro-symbolic AI integrating reasoning and learning for advanced cognitive systems. Intelligent Systems with Applications, 26, 200541. https://doi.org/10.1016/j.iswa.2025.200541 DOI

[9] Prenosil, G. A., Weitzel, T. K., Bello, S. C., Mingels, C., Manzini, G., Meier, L. P., Shi, K. Y., Rominger, A., & Afshar-Oromieh, A. (2025). Neuro-symbolic AI for auditable cognitive information extraction from medical reports. Communications Medicine, 5(1), 491. https://doi.org/10.1038/s43856-025-01194-x DOI

[10] Susskind, Z., Arden, B., John, L. K., Stockton, P., & John, E. B. (2021). Neuro-Symbolic AI: An Emerging Class of AI Workloads and their\n Characterization. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2109.06133 DOI