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Logic, Probability, and Hybrid Paradigms in Modern Artificial Intelligence: A Survey of Foundational and Contemporary Approaches

by Suhair Amer, Ankita Maharjan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 133
Year of Publication: 2026
Authors: Suhair Amer, Ankita Maharjan
10.5120/ijca0f5a03d5470f

Suhair Amer, Ankita Maharjan . Logic, Probability, and Hybrid Paradigms in Modern Artificial Intelligence: A Survey of Foundational and Contemporary Approaches. International Journal of Computer Applications. 187, 133 ( Aug 2026), 24-31. DOI=10.5120/ijca0f5a03d5470f

@article{ 10.5120/ijca0f5a03d5470f,
author = { Suhair Amer, Ankita Maharjan },
title = { Logic, Probability, and Hybrid Paradigms in Modern Artificial Intelligence: A Survey of Foundational and Contemporary Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2026 },
volume = { 187 },
number = { 133 },
month = { Aug },
year = { 2026 },
issn = { 0975-8887 },
pages = { 24-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number133/logic-probability-and-hybrid-paradigms-in-modern-artificial-intelligence-a-survey-of-foundational-and-contemporary-approaches/ },
doi = { 10.5120/ijca0f5a03d5470f },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-20T21:54:41.454329+05:30
%A Suhair Amer
%A Ankita Maharjan
%T Logic, Probability, and Hybrid Paradigms in Modern Artificial Intelligence: A Survey of Foundational and Contemporary Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 133
%P 24-31
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial intelligence research has historically been shaped by two dominant paradigms: logic-based symbolic reasoning and probability-based uncertainty modeling. While logic-based AI emphasizes formal deduction, structured knowledge, and explainability, probabilistic AI focuses on reasoning under uncertainty and incomplete evidence. More recently, these paradigms have converged in probabilistic logic systems and neuro-symbolic architectures, which integrate symbolic structure with statistical learning. This paper provides a structured review of these four interconnected strands—logic-based AI, probabilistic AI, probabilistic logic, and neuro-symbolic AI—highlighting their theoretical foundations, recent advances, and emerging convergence. The analysis shows that modern AI systems increasingly treat logic as a structure for reasoning, probability as a mechanism for uncertainty, and neural networks as a computational substrate that links both.

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Index Terms

Computer Science
Information Sciences

Keywords

Logic-based AI Probabilistic reasoning Neuro-symbolic AI Knowledge representation Hybrid intelligence

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