Integrating Neuro-Symbolic Approaches to Enhance Generalization in Artificial Intelligence Systems
Keywords:
Neuro-symbolic AI, generalization, hybrid architectures, knowledge representation, deep learning, reasoning systems, explainable AIAbstract
Despite recent advancements in deep learning, artificial intelligence (AI) systems often struggle to generalize across tasks, domains, and contexts due to their reliance on statistical pattern recognition. Neuro-symbolic AI, which integrates the learning capabilities of neural networks with the logical reasoning of symbolic systems, offers a promising paradigm to address this limitation. This paper investigates how hybrid neuro-symbolic architectures can enhance generalization in AI, proposing a framework that leverages symbolic knowledge structures to constrain and guide neural learning. We evaluate key components, review historical developments, and outline a modular system design. Our findings suggest that neuro-symbolic integration can significantly improve sample efficiency, explainability, and transfer learning capabilities, paving the way for more robust and cognitively aligned AI systems.
References
Bender, Emily M., and Alexander Koller. "Climbing towards NLU: On meaning, form, and understanding in the age of data." Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, pp. 5185–5198.
Nagamani, N. (2024). Multi-layer AI defense models against real-time phishing and deepfake financial fraud. ISCSITR - International Journal of Business Intelligence (ISCSITR-IJBI), 5(2), 7–21. https://doi.org/10.63397/ISCSITR-IJBI_05_02_02
Lake, Brenden M., Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. "Building machines that learn and think like people." Behavioral and Brain Sciences, vol. 40, 2017.
Winograd, Terry. Procedures as a Representation for Data in a Computer Program for Understanding Natural Language. MIT Technical Report, 1971.
Graves, Alex, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, and others. "Hybrid computing using a neural network with dynamic external memory." Nature, vol. 538, no. 7626, 2016, pp. 471–476.
Mao, Jiayuan, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu. "The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences from Natural Supervision." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019, pp. 1935–1944.
Nagamani, N. (2023). Hybrid AI Models Combining Financial NLP and Time-Series Forecasting for Stock Advisory. International Journal of Scientific Research in Artificial Intelligence and Machine Learning (ISCSITR-IJSRAIML), 4(1), 61–74. https://doi.org/10.63397/ISCSITR-IJSRAIML_2023_04_01_005
Serafini, Luciano, and Artur d’Avila Garcez. "Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge." arXiv preprint arXiv:1606.04422, 2016.
Johnson, Justin, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick. "CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017, pp. 1988–1997.
Yi, Kexin, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Joshua B. Tenenbaum. "Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding." Advances in Neural Information Processing Systems (NeurIPS), vol. 31, 2018.
Nagamani, N. (2023). Predictive AI models for reducing payment failures in digital wallet systems. International Journal of Fintech (IJFT), 2(1), 7–20. https://doi.org/10.34218/IJFT_02_01_002
Evans, Richard, and Edward Grefenstette. "Learning Explanatory Rules from Noisy Data." Journal of Artificial Intelligence Research, vol. 61, 2018, pp. 1–64.
Garcez, Artur d’Avila, and Luis C. Lamb. "Neurosymbolic AI: The Third Wave." arXiv preprint arXiv:2012.05876, 2020.
Marcus, Gary. "Deep learning: A critical appraisal." arXiv preprint arXiv:1801.00631, 2018.
Nagamani, N. (2024). AI-driven risk assessment models for health and life insurance underwriting. International Journal of Computer Science and Engineering (IACSE-IJCSE), 5(1). https://doi.org/10.5281/zenodo.17852768
Besold, Tarek R., Artur d’Avila Garcez, Sebastian Bader, Howard Bowman, Pedro M. Lima, Luis C. Lamb, and others. "Neural-Symbolic Learning and Reasoning: A Survey and Interpretation." arXiv preprint arXiv:1711.03902, 2017.
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Copyright (c) 2025 Laura Elizabeth (Author)
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