AIDec 1, 2014

Neutrosophic information in the framework of multi-valued representation

arXiv:1412.4802v18 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the problem of representing complex neutrosophic data for researchers in multi-valued logic and fuzzy systems, but it appears incremental as it builds on existing frameworks without claiming major breakthroughs.

The paper tackles the representation of neutrosophic information by developing steps for multi-valued representation using logical values like true, false, neutral, unknown, and saturated, and provides calculus formulae for features such as truth, falsity, neutrality, and entropy.

The paper presents some steps for multi-valued representation of neutrosophic information. These steps are provided in the framework of multi-valued logics using the following logical value: true, false, neutral, unknown and saturated. Also, this approach provides some calculus formulae for the following neutrosophic features: truth, falsity, neutrality, ignorance, under-definedness, over-definedness, saturation and entropy. In addition, it was defined net truth, definedness and neutrosophic score.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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