Knowledge Base Agents in Artificial Intelligence

Abstract

Knowledge Representation (KR) in AI focuses on structuring real-world information (facts, concepts, rules) into formal systems (logic, graphs, ontologies) that computers can process for tasks like reasoning, planning, and problem-solving, bridging the gap between human understanding and machine computation by making knowledge explicit and usable for intelligent agents.

Introduction

Knowledge Representation (KR) is a fundamental area of Artificial Intelligence that focuses on encoding real-world facts, rules, and relationships into structured, computer-readable forms so that AI systems can reason, infer, and make intelligent decisions. KR goes beyond simple data storage by enabling meaningful understanding and problem-solving in domains such as robotics, healthcare, and natural language processing.

A key application of KR is in knowledge-based agents, which rely on a knowledge base (KB) containing sentences expressed in a formal representation language. These agents interact with their environment using two main operations: TELL, to add new knowledge based on percepts or actions, and ASK, to query the KB to decide the best action. The declarative approach to system design emphasizes explicit representation and reasoning, while the procedural approach encodes behavior directly in program logic.

The Wumpus World is used as a classic example to illustrate knowledge-based reasoning. In this environment, an agent uses logical inference from sensory percepts—such as stench, breeze, and glitter—to deduce safe and unsafe locations, identify the presence of pits or the wumpus, and ultimately reach the goal of collecting gold. The agent’s conclusions are logically sound, demonstrating the reliability of formal reasoning in AI.

The text also introduces logic as the foundation of KR, defining syntax and semantics to determine truth across possible models. Logical entailment explains how new truths can be inferred from existing knowledge. Propositional Logic, the simplest form of logic, is presented as a common KR technique, using propositions, logical connectives (¬, ∧, ∨, →, ⇔), and truth tables to represent and evaluate knowledge precisely.

Overall, the text highlights how formal logic and structured knowledge representation enable AI systems to reason correctly, draw guaranteed conclusions, and behave intelligently in complex and uncertain environments.

Conclusion

Knowledge-based agents in AI are a major breakthrough. They can reason, learn, and make decisions, changing how we use machines. These agents are more than just tools—they inspire innovation. From healthcare to finance, they create smarter solutions. Of course, challenges like ethics and scalability exist. But the benefits far outweigh the problems. As research advances, combining human skills with AI will unlock amazing possibilities. Whether it’s simplifying tasks or solving tough problems, these agents are ready to change the game. They aren’t just improving technology—they’re reshaping how we live, work, and grow.

References

[1] Artificial Intelligence – A modern approach : Stuart Russell, Peter Norvig, 2nd Edition [2] https://www.geeksforgeeks.org/artificial-intelligence/knowledge-representation-in-ai/ [3] https://www.youngurbanproject.com/knowledge-based-agents-in-ai/

Copyright

Copyright © 2026 Nethravathi H , Mangesh S Pai, Raziya Sultana, Siddarth Y K, Tejaswini B H, Manoj H M, Kirankumar M G, Divya R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.