AI-Powered Fuzzy Multi -Criteria Decision -Making: Leveraging Yager’s Approach for Improved Outcomes in Autonomous Systems

In our rapidly evolving world, where autonomous systems play a crucial role in decision-making, it is essential to develop effective methods for managing uncertainty. This paper presents a novel approach to multi-criteria decision-making (MCDM) using an AI-powered fuzzy method based on Yager's technique. We explore how fuzzy logic enhances the evaluation of renewable energy technologies by converting qualitative insights into measurable fuzzy membership scores. By applying this framework to assess solar, wind, and hydro energy options, we analyze key factors such as cost, efficiency, environmental impact, reliability, and social acceptance. Our findings reveal clear rankings and trade-offs among these alternatives, providing decision-makers with a comprehensive understanding of how their choices align with stakeholder values. The results underscore the potential of fuzzy MCDM to inform strategic decisions in the renewable energy sector and beyond, paving the way for improved outcomes in autonomous systems. This work serves as a vital step toward integrating AI-driven methods in complex decision-making contexts where traditional approaches may fall short.

Keywords:

Fuzzy Multi -Criteria , Decision Making , Yager’s Approach , Autonomous Systems , Artificial Intelligence