Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Neurosymbolic Cognitive Computing Frameworks with Self Learning Capabilities for Global Community Engagement and Policy Making

Authors
Karthik Chava1, J. A. Bagawade2, *, C. Shahin Banu3, *, P. Mathiyalagan4, Dibyahash Bordoloi5, 6, K. Alagarraja7
1Senior Software Engineer, knipper Princeton, NJ, USA
2Vidya Pratishthan’s Arts Science and Commerce College, Baramati, Maharashtra, India
3Assistant Professor, Department of English, Sona College of Technology, Salem, Tamil Nadu, India
4Professor, Department of Mechanical Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
5Associate Professor, Computer Science and Engineering, Graphic Era Hill University, Dehradun, India
6Adjunct Professor, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
7Assistant Professor, Department of Mech, new prince shri bhavani college of engineering and technology, Chennai, Tamil Nadu, India
*Corresponding author.
*Corresponding author. Email: chimanpurejayu@gmail.com
Corresponding Authors
J. A. Bagawade, C. Shahin Banu
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_162How to use a DOI?
Keywords
Neurosymbolic AI; cognitive computation; self-learning; global community participation; policy-making; decision support
Abstract

This work contributes to the development of self-learning neurosymbolic cognitive computing frameworks to enrich global community engagement and policy-making. The current literature has accomplished much in terms of the theoretical groundwork for neurosymbolic AI, but some challenges such as a lack of sufficient integration with real-time policy support, self-learning models of limited use, and the under-explored opportunity of broadening the focus of symbolic reasoning are still left and need to be discussed and addressed in order to progress. This research responds to the aforementioned challenges by proposing a powerful, hybrid AI model which integrates symbolic reasoning together with machine learning methods, allowing for on-the-fly decision making, learning ability and scalability in multifaceted policy environments. This fosters a cross-disciplinary heuristic approach via merging insights from cognitive science, policy studies, and AI, leading to an actionable framework for decision support in policy-making. Using the dynamic and stage-maturing nature of AI technology itself one can self-learn the global scanned data and thus transmute these global shortlisted data into proactive insights for policy-makers to potentially fight chronic global challenges’ like Climate Change, Health, Social Inequality etc. Vastly underused within the governance domain, this work establishes the efficacy of neurosymbolic cognitive computing as a tool for positive transformation, enabling more resilient, adaptive communities across the globe.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_162How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Karthik Chava
AU  - J. A. Bagawade
AU  - C. Shahin Banu
AU  - P. Mathiyalagan
AU  - Dibyahash Bordoloi
AU  - K. Alagarraja
PY  - 2025
DA  - 2025/05/23
TI  - Neurosymbolic Cognitive Computing Frameworks with Self Learning Capabilities for Global Community Engagement and Policy Making
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1964
EP  - 1976
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_162
DO  - 10.2991/978-94-6463-718-2_162
ID  - Chava2025
ER  -