Hybrid Learning Systems - Integration of AI Techniques: Investigating hybrid learning systems combining neural networks with symbolic reasoning or evolutionary algorithms for enhanced AI
Keywords:
Hybrid Learning Systems, Artificial Intelligence, Neural NetworksAbstract
Hybrid Learning Systems (HLS) represent a powerful approach to artificial intelligence (AI) by integrating diverse AI techniques, particularly neural networks, symbolic reasoning, and evolutionary algorithms. This paper investigates the current state and potential of HLS in enhancing AI capabilities. We explore the theoretical foundations, design considerations, and practical implementations of HLS, highlighting the advantages and challenges of combining these techniques. Through a comprehensive review and analysis, we aim to provide insights into the development and application of HLS for future AI systems.
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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.
Prabhod, Kummaragunta Joel. "Advanced Techniques in Reinforcement Learning and Deep Learning for Autonomous Vehicle Navigation: Integrating Large Language Models for Real-Time Decision Making." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 1-20.
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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