Abstract
Knowledge Representation and Reasoning (KRR) is a cornerstone of artificial intelligence (AI), providing machines with the capability to model, store, and reason about information in a way that mimics human intelligence. KRR facilitates understanding, decision-making, and problem-solving by formalizing knowledge in structured forms such as logic, semantic networks, ontologies, and probabilistic models. This paper presents a comprehensive review of knowledge representation paradigms, reasoning mechanisms, and their applications across various AI domains, including expert systems, natural language understanding, robotics, and intelligent agents. Additionally, the paper discusses challenges in scalability, uncertainty handling, and real-time reasoning, offering insights into future research directions in KRR.
Keywords: Knowledge Representation, Reasoning, Ontologies, Semantic Networks, Logic-Based AI, Probabilistic Reasoning, Expert Systems, Artificial Intelligence
Full Issue
| View or download the full issue | PDF 59-70 |