A Fuzzy Logic Framework for AI Decision-Making Under Uncertainty
Keywords:
AI decision-making, Approximate reasoning, Fuzzy logic, Hybrid intelligent systems, Interpretable AI, Membership functions, Neuro-fuzzy systemsAbstract
Artificial intelligence systems are increasingly required to operate in complex, dynamic environments characterized by incomplete, imprecise, ambiguous, and conflicting information. Classical binary logic and purely probabilistic models frequently prove inadequate when confronted with linguistic uncertainty, graded membership of concepts, and the need for human-like approximate reasoning. This paper presents a comprehensive fuzzy-logical framework specifically designed for AI-based decision-making under uncertainty. The framework systematically integrates fuzzy set theory, multi-style fuzzy inference systems (Mamdani, Sugeno, and Tsukamoto), and hybrid neuro-fuzzy architectures to support robust, transparent, and adaptive decision processes. This study formalizes the representation of multiple forms of uncertainty, details the complete inference pipeline from fuzzification through defuzzification, and introduces adaptive learning mechanisms for membership functions and rule weights. A formal decision model is derived that ranks alternatives using defuzzified scores, possibility measures, and fuzzy ranking indices. An experimental study on a simulated multi-criteria medical risk-assessment task and an autonomous navigation scenario demonstrates that the proposed framework achieves higher interpretability and competitive predictive performance compared with pure neural baselines, while remaining data-efficient through the incorporation of expert linguistic knowledge. The approach enhances transparency, resilience, and human–AI collaboration in domains such as autonomous systems, clinical decision support, financial risk assessment, and intelligent control.
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