A Systematic Review of Research Gaps and Future Directions for Generalizable Autonomous Systems for Causal and Neuro-Symbolic Artificial Intelligence
Keywords:
Causal artificial intelligence, Explainable artificial intelligence, Generalizable autonomous systems, Neuro-symbolic learning, Self-learning autonomous intelligenceAbstract
Autonomous artificial intelligence systems continue to rely heavily on correlation-based learning mechanisms. The limited application of causal learning mechanisms has resulted in poor causal reasoning, low logical consistency, low adaptability, and low transparency in autonomous decision formation. This systematic review aims to critically evaluate existing research with respect to the incorporation of autonomous artificial intelligence models with causal representation learning mechanisms for effective decision formation, neural learning with symbolic reasoning for logical consistency, and generalization with respect to unseen operational scenarios. The quantitative synthesis has revealed that fewer than 30% of artificial intelligence models incorporate causal modelling mechanisms, while integrated neuro-symbolic models are limited to 25% of intelligent system frameworks. In addition, almost 60% of frameworks are found to be devoid of continuous self-learning mechanisms with respect to adaptive feedback, while more than 65% are found to be devoid of interpretable reasoning chains with respect to autonomous decision formation. The gaps with respect to autonomous artificial intelligence frameworks highlight the need for integrated frameworks with respect to unified causal-neuro-symbolic models with self-learning, explainable, and generalizable characteristics.
References
B. Scholkopf et al., “Toward causal representation learning,” Proceedings of the IEEE, vol. 109, no. 5, pp. 612–634, May 2021.
E. Bareinboim and J. Pearl, “Causal inference and the data-fusion problem,” Proceedings of the National Academy of Sciences, vol. 113, no. 27, pp. 7345–7352, Jul. 2016.
R. Plotnick, Power button: A history of pleasure, panic, and the politics of pushing. Cambridge, MA, USA: MIT Press, 2018.
D. You, J. Shen, Z. Li, C. Lu, Z. Chen and X. Wu, “Causal representation learning via graph attention mechanism with aggregating causal information,” Knowledge-Based Systems, vol. 334, Feb. 2026.
A. S. d'Avila Garcez, L. C. Lamb and D. M. Gabbay, Neural-symbolic cognitive reasoning. Berlin, Germany: Springer, 2009.
T. R. Besold et al., “Neural-symbolic learning and reasoning: A survey and interpretation,” arXiv, 2017.
I.-J. Kim, “Surface engineering for safer walking environments: Optimising floor coatings for enhanced slip resistance,” Results in Engineering, vol. 25, Mar. 2025.
K. Acharya and H. Song, “A comprehensive review of neuro-symbolic AI for robustness, uncertainty quantification, and intervenability,” Arabian Journal for Science and Engineering, vol. 51, pp. 35–67, Dec. 2025,
B. C. Colelough and W. Regli, “Neuro-symbolic AI in 2024: A systematic review,” arXiv, Jan. 2025.
B. Liang, Y. Wang and C. Tong, “AI reasoning in deep learning era: From symbolic AI to neural–symbolic AI,” Mathematics, vol. 13, no. 11, May 2025.
Z. Wan et al., “Towards efficient neuro-symbolic AI: From workload characterization to hardware architecture,” in IEEE Transactions on Circuits and Systems for Artificial Intelligence, vol. 1, no. 1, pp. 53–68, Sept. 2024.
L. Han and M. B. Srivastava, “An empirical evaluation of neural and neuro-symbolic approaches to real-time multimodal complex event detection,” arXiv, Mar. 2024.
D. Silver, S. Singh, D. Precup and R. S. Sutton, “Reward is enough,” Artificial Intelligence, vol. 299, Oct. 2021.
S. Grönroos, M. Pierini and N. Chernyavskaya, “Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier,” Machine Learning: Science and Technology, vol. 4, Aug. 2023.
Y. Wang and M. I. Jordan, “Desiderata for representation learning: A causal perspective,” Journal of Machine Learning Research, vol. 25, no. 275, pp. 1–52, 2024.
S. Barat, V. Kulkarni, A. Paranjape, S. Dhandapani, S. Manuelraj and S. P. Parameswaran, “Agent based digital twin of sorting terminal to improve efficiency and resiliency in parcel delivery,” Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection, 2022, pp. 24–35.
C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence, vol. 1, pp. 206–215, May 2019.
W. Samek, T. Wiegand, and K.-R. Müller, “Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models,” ITU Journal, no. 1, 2017.
A. Holzinger, A. Carrington, and H. Müller, “Measuring the quality of explanations: The system causability scale (SCS),” KI - Künstliche Intelligenz, vol. 34, pp. 193–198, Jan. 2020.
S. Gorla, L. B. M. Neti, and A. Malapati, “Enhancing the performance of Telugu named entity recognition using gazetteer features,” Information, vol. 11, no. 2, Feb. 2020.
W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K.-R. Müller, Eds., Explainable AI: Interpreting, explaining and visualizing deep learning. Cham, Switzerland: Springer, 2019.
D. E. Mathew, D. U. Ebem, A. C. Ikegwu, P. E. Ukeoma and N. F. Dibiaezue, “Recent emerging techniques in explainable artificial intelligence to enhance the interpretable and Understanding of AI models for human,” Neural Processing Letters, vol. 57, Feb. 2025.
D. R and V. B. Kumaravelu, “Outage analysis and power optimization in uplink and downlink NOMA systems with Rician fading,” Results in Engineering, vol. 25, Mar. 2025.
S. Kabir, M. S. Hossain and K. Andersson, “A review of explainable artificial intelligence from the perspective of challenges and opportunities,” Algorithms, vol. 18, no. 9, Sept. 2025.
A. Johannssen, P. Qiu, A. Yeganeh, and N. Chukhrova, “Explainable AI for trustworthy intelligent process monitoring,” Computers & Industrial Engineering, vol. 209, Nov. 2025.
A. Almadhor, S. Alsubai, A. A. Hejaili, Z. Klai, B. Bouallegue and U. Kovac, “Designing a neuro-symbolic dual-model architecture for explainable artificial intelligence systems,” Scientific Reports, vol. 15, Nov. 2025.
D. Hossain and J. Y Chen, “A study on neuro-symbolic artificial intelligence: Healthcare perspectives,” arXiv, Mar. 2025.
R. Ahmed and S. EP, “An intelligent phishing email detection system using ensemble methods and explainable AI,” Knowledge-Based Systems, vol. 337, Mar. 2026.
P. Gupta, B. Ding, C. Guan, and D. Ding, “Generative AI: A systematic review using topic modelling techniques,” Data and Information Management, vol. 8, no. 2, Jun. 2024.
K. Friston, “The free-energy principle: a unified brain theory?,” Nature Reviews Neuroscience, vol. 11, pp. 127–138, Jan. 2010.
H. Xiong et al., “Converging paradigms: The synergy of symbolic and connectionist AI in LLM-empowered autonomous agents,” arXiv, 2024.
L. De Raedt, K. Kersting, S. Natarajan, and D. Poole, Eds., Statistical relational artificial intelligence. Cham, Switzerland: Springer International Publishing, 2016.
