Neutrosophic Logic as a Framework for Managing Uncertainty in Artificial Intelligence

Abstract

Artificial Intelligence systems are increasingly required to operate in environments 
characterized by incomplete, ambiguous, and contradictory information. Traditional probabilistic 
and fuzzy-logic approaches often compress uncertainty into a single scalar value, limiting their 
ability to distinguish between ignorance, conflict, and evidential support. This paper explores the 
conceptual and practical relationship between Neutrosophic Logic and modern Artificial 
Intelligence architectures. The neutrosophic framework represents knowledge through three 
independent dimensions—Truth (T), Indeterminacy (I), and Falsity (F)—thereby providing a richer 
representation of uncertainty than conventional binary or probabilistic models. The study examines 
how neutrosophic triplets can be implemented as software primitives and applied in Retrieval
Augmented Generation (RAG) systems, multi-agent architectures, cybersecurity workflows, and 
AI-assisted decision-making. Particular attention is given to the role of indeterminacy as a 
measurable and actionable variable that enables systems to recognize incomplete knowledge, avoid 
hallucinations, and improve reliability. The paper argues that the integration of neutrosophic 
principles into AI engineering offers a promising pathway toward more robust, transparent, and 
trustworthy intelligent systems capable of operating under real-world uncertainty. 

DOI 10.5281/zenodo.20673052