CFP last date
20 November 2026
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2026

Submit your paper
Know more
The week's pick
Responsive image
A Machine Learning-based Decision Framework for Demand Forecasting and Price Optimization in Competitive E-Commerce Environments

Aera K. Leboulluec Janhavi Datye
Random Articles
Reseach Article

Role of First Order Predicate Logic in Knowledge Acquisition and Representation in Artificial Intelligence

by M. Mary Sujatha
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 149
Year of Publication: 2026
Authors: M. Mary Sujatha
10.5120/ijcac19046ca803b

M. Mary Sujatha . Role of First Order Predicate Logic in Knowledge Acquisition and Representation in Artificial Intelligence. International Journal of Computer Applications. 187, 149 ( Oct 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 },
issue_date = { Oct 2026 },
volume = { 187 },
number = { 149 },
month = { Oct },
year = { 2026 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number149/role-of-first-order-predicate-logic-in-knowledge-acquisition-and-representation-in-artificial-intelligence/ },
doi = { 10.5120/ijcac19046ca803b },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-10-24T03:08:53.881005+05:30
%A M. Mary Sujatha
%T Role of First Order Predicate Logic in Knowledge Acquisition and Representation in Artificial Intelligence
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 149
%P 33-36
%D 2026
%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.

References
  1. Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  2. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou,Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z.
  3. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016.
  4. Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  5. Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  6. Belle, V. (2023). Knowledge representation and acquisition for ethical AI. Springer.
  7. Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
  8. Delgrande, J. P., Glimm, B., Meyer, T., Truszczynski, M., & Wolter, F. (2023). Current and Future Challenges in Knowledge Representation and Reasoning. arXiv:2308.04161.
  9. Yang, Z., et al. (2023). Logical Reasoning over Natural Language as Knowledge Representation: A Survey. arXiv:2303.12023.
  10. Pan, L., Albalak, A., Wang, X., & Wang, W. (2023). Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning. arXiv:2305.12295.
  11. Nawaz, U., et al. (2025). A Review of Neuro-Symbolic AI Integrating Reasoning and Learning. AI Open, Elsevier.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence (AI) Knowledge Acquisition Knowledge Representation First-Order Predicate Logic (FOPL)

Powered by PhDFocusTM