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A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution

by Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 67
Year of Publication: 2025
Authors: Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku
10.5120/ijca2025924503

Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku . A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution. International Journal of Computer Applications. 186, 67 ( Feb 2025), 9-20. DOI=10.5120/ijca2025924503

@article{ 10.5120/ijca2025924503,
author = { Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda, Praveen Chaitanya Jakku },
title = { A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2025 },
volume = { 186 },
number = { 67 },
month = { Feb },
year = { 2025 },
issn = { 0975-8887 },
pages = { 9-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number67/a-unified-framework-for-self-learning-ai-reinforcement-learning-neural-search-and-adaptive-evolution/ },
doi = { 10.5120/ijca2025924503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-25T22:58:01.876833+05:30
%A Praveen Kumar Myakala
%A Chiranjeevi Bura
%A Anil Kumar Jonnalagadda
%A Praveen Chaitanya Jakku
%T A Unified Framework for Self-Learning AI: Reinforcement Learning, Neural Search, and Adaptive Evolution
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 67
%P 9-20
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study explores the shift from explicitly programmed systems to machines capable of autonomous learning and adaptation, addressing the scalability and flexibility limitations of traditional programming. By integrating advanced machine learning, reinforcement learning, and self-evolving algorithms, this study aims to establish principles that enable machines to process information autonomously, adapt behaviors, and operate in dynamic, unstructured environments. Key challenges, such as ensuring system safety and robustness, are examined along with practical applications in robotics, personalized healthcare, and adaptive AI systems. This study lays the foundation for next-generation adaptive agents, providing a transformative framework to achieve true autonomy in artificial intelligence.

References
  1. Comparing artificial neural networks and traditional algorithms: A comprehensive analysis in machine learning, 2022. Accessed: 2025-01-04.
  2. Kerem G¨ulen. All about autonomous intelligence: A comprehensive overview, 2023. Accessed: 2025-01-04.
  3. Rodney A Brooks. Intelligence without representation. Artificial intelligence, 47(1-3):139–159, 1991.
  4. Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
  5. David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484–489, 2016.
  6. Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. Meta-learning in neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(9):5149–5169, 2020.
  7. Glenn Richards. Swarm behavior and evolutionary algorithms: Future of nature-inspired computing. Walsh Medical Media, 2024.
  8. Jagdish Chand Bansal and Nikhil R. Pal. Swarm and Evolutionary Computation, pages 1–9. Springer International Publishing, Cham, 2019.
  9. Luong Vuong Nguyen. Swarm intelligence-based multirobotics: A comprehensive review. AppliedMath, 4(4):1192– 1210, 2024.
  10. Greg et al. Brockman. Openai gym. In arXiv preprint, 2016.
  11. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  12. Kun-Hsing Yu, Andrew L Beam, and Isaac S Kohane. Artificial intelligence in healthcare. Nature biomedical engineering, 2(10):719–731, 2018.
  13. Alejandro Barredo Arrieta et al. Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information Fusion, 58:82–115, 2020.
  14. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464):447– 453, 2019.
  15. Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. Neural architecture search: A survey. Journal of Machine Learning Research, 20(55):1–21, 2019.
  16. George Papanastasiou, Dimitrios Karras, and Yannis Voulgaris. Adaptive learning for autonomous navigation in dynamic environments. Robotics and Autonomous Systems, 141:103709, 2021.
  17. Caterina Neef, Dario Luipers, Jan Bollenbacher, Christian Gebel, and Anja Richert. Towards intelligent pick and place assembly of individualized products using reinforcement learning. In Human Systems Engineering and Design III: Proceedings of the 3rd International Conference on Human Systems Engineering and Design (IHSED2020): Future Trends and Applications, September 22-24, 2020, Juraj Dobrila University of Pula, Croatia 3, pages 325–331. Springer, 2021.
  18. BoWang, Jie Zhang, Jian Cui, and Zhaoqi Li. Swarm robotics with adaptive coordination for cooperative construction. Autonomous Robots, 46(4):487–504, 2022.
  19. Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen AWM Van Der Laak, Bram Van Ginneken, and Clara I S´anchez. A survey on deep learning in medical image analysis. Medical Image Analysis, 42:60–88, 2017.
  20. Anthony R Lanfranco, Andres E Castellanos, Jaydev P Desai, and William C Meyers. Robotic surgery: a current perspective. Annals of surgery, 239(1):14–21, 2004.
  21. Wei Zhang, Ming Chen, and Jing Liu. Dynamic path planning based on artificial potential field method for autonomous vehicles on icy roads. Sustainability, 15(21):15377, 2023.
  22. Jangwon Choi, Hanseok Kim, and Hyoungseob Ko. Sensor fusion in autonomous driving: A survey on model-based and learning-based methods. Annual Review of Control, Robotics, and Autonomous Systems, 3:319–344, 2020.
  23. William Uther and Manuela Veloso. Adversarial reinforcement learning. Technical report, Tech. rep., Carnegie Mellon University. Unpublished, 1997.
  24. Marco Wiering, Johan van Veenen, Jelle Vreeken, and Ard Koopman. Intelligent traffic light control. Engineering Applications of Artificial Intelligence, 13(4):407–416, 2000.
  25. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.
  26. Zhengyu Zhao, Lijun Qin, Kai Wang, Zhenyu Li, and Xingguo Du. Ai in public safety applications. IEEE Access, 7:67989–67999, 2019.
  27. Praveen Kumar Myakala, Anil Kumar Jonnalagadda, and Chiranjeevi Bura. Federated learning and data privacy: A review of challenges and opportunities. International Journal of Research Publication and Reviews, 5(12):10–55248, 2024.
  28. Paul Voigt and Axel Von dem Bussche. The EU General Data Protection Regulation (GDPR). Springer, 2017.
  29. Emma Strubell, Ananya Ganesh, and Andrew McCallum. Energy and policy considerations for deep learning in nlp. arXiv preprint arXiv:1906.02243, 2019.
  30. High-Level Expert Group on Artificial Intelligence. Ethics guidelines for trustworthy ai. European Commission, 2019.
  31. Praveen Kumar Myakala, Chiranjeevi Bura, and Anil Kumar Jonnalagadda. Artificial immune systems: A bio-inspired paradigm for computational intelligence. Journal of Artificial Intelligence and Big Data, 5(1):10–31586, 2025.
  32. Praveen Kumar Myakala, Anil Kumar Jonnalagadda, and Chiranjeevi Bura. The human factor in explainable ai frameworks for user trust and cognitive alignment. International Advanced Research Journal in Science, Engineering and Technology, 12(1):80–93, 2025.
Index Terms

Computer Science
Information Sciences
Artificial Intelligence
Autonomous Systems
Machine Learning
Adaptive Algorithms
Reinforcement Learning
Self-Evolving Systems
Neural Networks
Robotics
Data-Driven Decision Making
Emergent Behavior

Keywords

Autonomous Machine Intelligence Reinforcement Learning Self-Evolving Algorithms Adaptive AI Systems Neural Architecture Search

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