From Fuzzy Logic to Neutrosophic Sets: A Guide to Handling Real-World Uncertainty
Table of Contents Introduction The Problem: Why Traditional Logic Fails Fuzzy Sets: The First Step Beyond Black and White Intuitionistic Fuzzy Sets: Adding Degrees of Disbelief Neutrosophic Sets: Embracing True Indeterminacy Plithogenic Sets: The Next Evolution Real-World Applications Key Concepts to Remember Why This Matters for AI and Beyond Conclusion Resources Introduction Imagine you’re building an AI system to diagnose a disease. A patient comes in with symptoms that could indicate condition A, condition B, or possibly neither—but you’re not entirely sure. Traditional computer logic forces you into a corner: either the patient has the disease or they don’t. True or false. 1 or 0. But reality doesn’t work that way. ...