Research Article
Role of First Order Predicate Logic in Knowledge Acquisition and Representation in Artificial Intelligence
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 149 |
| Published: October 2026 |
| Authors: M. Mary Sujatha |
10.5120/ijcac19046ca803b
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M. Mary Sujatha . Role of First Order Predicate Logic in Knowledge Acquisition and Representation in Artificial Intelligence. International Journal of Computer Applications. 187, 149 (October 2026), 33-36. DOI=10.5120/ijcac19046ca803b
@article{ 10.5120/ijcac19046ca803b,
author = { M. Mary Sujatha },
title = { Role of First Order Predicate Logic in Knowledge Acquisition and Representation in Artificial Intelligence },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 149 },
pages = { 33-36 },
doi = { 10.5120/ijcac19046ca803b },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A M. Mary Sujatha
%T Role of First Order Predicate Logic in Knowledge Acquisition and Representation in Artificial Intelligence%T
%J International Journal of Computer Applications
%V 187
%N 149
%P 33-36
%R 10.5120/ijcac19046ca803b
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The role of First-Order Predicate Logic (FOPL) in Artificial Intelligence (AI) is foundational to effective knowledge acquisition and representation. As intelligent systems increasingly rely on structured reasoning, FOPL provides a rigorous and expressive framework to model objects, relationships, and quantifiers. This paper explores how FOPL facilitates the transformation of raw knowledge into machine-usable formats, enabling inference, consistency checking, and decision-making. The discussion highlights the importance of declarative and procedural knowledge, the use of inference mechanisms such as unification and resolution, and their applications in expert systems, knowledge bases, and natural language processing. By examining the integration of FOPL into knowledge acquisition processes, this study underscores its significance in bridging human cognition and computational intelligence. The findings reaffirm FOPL as a cornerstone of symbolic AI and as a vital tool for advancing explainable and reliable intelligent systems.
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