Artificial Intelligence and Knowledge-Based Systems for Complex Problem Solving

Anand Dhruva

Artificial Intelligence and Knowledge-Based Systems for Complex Problem Solving

Author(s)	Anand Dhruva
Country	United States
Abstract	Artificial Intelligence (AI) and Knowledge-Based Systems (KBS) have emerged as important technologies for solving complex problems that involve uncertainty, large-scale data, multiple variables, and dynamic decision-making environments. Traditional computational approaches often depend on predefined instructions and may have limited ability to adapt to unfamiliar situations. AI-based approaches, including machine learning, deep learning, natural language processing, reinforcement learning, and intelligent optimisation, provide mechanisms for identifying patterns and generating predictions from large datasets. Knowledge-Based Systems complement these approaches by representing domain-specific knowledge through rules, ontologies, semantic networks, knowledge graphs, and inference mechanisms. This study examines the role of AI and Knowledge-Based Systems in complex problem solving across healthcare, finance, manufacturing, agriculture, environmental management, cybersecurity, education, and public administration. A qualitative and conceptual research methodology based on secondary literature is employed. The study analyses the complementary strengths of data-driven and knowledge-driven approaches and proposes a hybrid intelligent problem-solving framework integrating machine learning, knowledge representation, symbolic reasoning, uncertainty management, explainability, and human oversight. The analysis indicates that hybrid systems can potentially improve decision accuracy, adaptability, consistency, and interpretability compared with approaches based exclusively on either predefined rules or statistical learning. However, challenges involving data quality, knowledge acquisition, bias, explainability, computational complexity, cybersecurity, knowledge updating, and human accountability remain significant. The study concludes that future intelligent problem-solving systems are likely to combine learning and reasoning capabilities to support more reliable, transparent, and context-aware decision-making.
Keywords	: Artificial Intelligence, Knowledge-Based Systems, Complex Problem Solving, Machine Learning, Knowledge Representation, Expert Systems, Knowledge Graphs, Symbolic AI, Hybrid AI, Decision Support.
Field	Engineering
Published In	Volume 4, Issue 5, September-October 2022
Published On	2022-09-04

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Artificial Intelligence and Knowledge-Based Systems for Complex Problem Solving

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