Neuro symbolic Computing - Integration and Applications: Exploring approaches for integrating symbolic reasoning with neural networks to enable more interpretable and flexible AI systems

Authors

  • Dr. Marc Hansenne Professor of Geomatics Engineering, Université Laval, Canada Author

Keywords:

Neuro-symbolic computing

Abstract

Neuro-symbolic computing represents a promising paradigm for AI, combining the strengths of symbolic reasoning and neural networks. This paper explores the integration of these two approaches, aiming to enhance the interpretability and flexibility of AI systems. We survey existing methods for neuro-symbolic computing and analyze their applications across various domains. Additionally, we discuss challenges and future directions in this field, highlighting the potential impact of neuro-symbolic computing on advancing AI research and applications.

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References

Tatineni, S., and A. Katari. “Advanced AI-Driven Techniques for Integrating DevOps and MLOps: Enhancing Continuous Integration, Deployment, and Monitoring in Machine Learning Projects”. Journal of Science & Technology, vol. 2, no. 2, July 2021, pp. 68-98, https://thesciencebrigade.com/jst/article/view/243.

K. Joel Prabhod, “ASSESSING THE ROLE OF MACHINE LEARNING AND COMPUTER VISION IN IMAGE PROCESSING,” International Journal of Innovative Research in Technology, vol. 8, no. 3, pp. 195–199, Aug. 2021, [Online]. Available: https://ijirt.org/Article?manuscript=152346

Tatineni, Sumanth, and Sandeep Chinamanagonda. “Leveraging Artificial Intelligence for Predictive Analytics in DevOps: Enhancing Continuous Integration and Continuous Deployment Pipelines for Optimal Performance”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Feb. 2021, pp. 103-38, https://aimlstudies.co.uk/index.php/jaira/article/view/104.

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Published

30-12-2023

How to Cite

[1]
Dr. Marc Hansenne, “Neuro symbolic Computing - Integration and Applications: Exploring approaches for integrating symbolic reasoning with neural networks to enable more interpretable and flexible AI systems”, Australian Journal of Machine Learning Research & Applications, vol. 3, no. 2, pp. 251–259, Dec. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/62

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