Fuzzy computing is a branch of artificial intelligence that deals with reasoning and decision-making using degrees of truth represented by real numbers from interval [0,1]. Such representation of truth values is useful when dealing with imprecise or uncertain data. Neural-symbolic computing is a research that aims at integrating the ability of neural networks to learn from the environment and the ability to reason using symbolic representation of knowledge. These two domains are closely related because neural networks also yield real numbers. Please see my relevant research papers:

• Fuzzy Logic Based on Zadeh Operators
• Inference in Extensions of Intuitionistic Logic as Fuzzy Computing
• Fuzzy Non-Horn Knowledge Bases: Calculi, Models, Inference
• Calculi and Models for Non-Horn Knowledge Bases Containing Neural and Evaluable Predicates
• Symbolic Inference for Non-Horn Knowledge Bases With Fuzzy Predicates
• Heuristic Backward Chaining Based on Predicate Tensorization
• A Best-First Backward-Chaining Search Strategy Based on Learned Predicate Representations
• Hierarchical Rules for Knowledge Representation and Learning

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