A Comparative Evaluation of Algorithmic Paradigms in Autonomous Decision-Making Systems
Keywords:
autonomous decision-making systems, reinforcement learning, expert systems, fuzzy logic, algorithmic paradigms, AI-driven models, computational efficiency, systemic robustness, hybrid frameworksAbstract
This paper presents a comparative evaluation of contemporary algorithmic paradigms utilized in autonomous decision-making systems (ADMS), emphasizing their distinct operational frameworks and performance metrics. The study employs a rigorous empirical methodology, leveraging quantitative analysis to assess the execution efficiency, adaptability, and decision accuracy across various AI-driven models, including reinforcement learning, expert systems, and fuzzy logic approaches. Results indicate significant disparities in output reliability and computational overhead, with reinforcement learning demonstrating superior adaptability in dynamic environments. This analysis not only elucidates the strengths and weaknesses inherent to each paradigm but also proposes a hybrid framework that amalgamates the most effective elements of these methodologies for enhanced systemic robustness. Findings have far-reaching implications for the development of next-generation intelligent systems in robotics, healthcare, and autonomous vehicles.
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