Author: Shakya Singha Guria
DOI Link: https://doi.org/10.70798/PP/020400030
Abstract: Artificial Intelligence (AI) has evolved from rule-based symbolic reasoning systems to highly adaptive learning models capable of solving complex, real-world problems. However, real-world environments are inherently uncertain, imprecise, nonlinear, and dynamic. Classical computational paradigms, often termed “hard computing,” rely on exact logic, binary truth values, and deterministic models that struggle under such imperfect conditions. Soft computing emerged as a powerful alternative paradigm designed to exploit tolerance for imprecision, uncertainty, and partial truth in order to achieve tractable, robust, and cost-effective solutions. This research article presents a comprehensive and analytically detailed exploration of the role of soft computing techniques—such as Fuzzy Logic, Artificial Neural Networks, Genetic Algorithms, Swarm Intelligence, Probabilistic Reasoning, and Hybrid Systems—in Artificial Intelligence systems. Through extended conceptual discussion, methodological analysis, domain-based applications, and future research directions, the paper argues that soft computing constitutes one of the foundational pillars of modern AI, enabling systems to operate in complex, ambiguous, and data-intensive environments.
Keywords: Soft Computing; Artificial Intelligence (AI); Fuzzy Logic; Genetic Algorithms (GA); Swarm Intelligence (SI); Hybrid Intelligent Systems; Quantum-Inspired Algorithms.
Page No: 217-224