Uncertainty suffuses our daily lives and spans the whole gamut from stock market index fluctuations to weather prediction, to car parking in crowded areas, to traffic control. For almost every domain that we can think of, imprecision or ambiguity is very much present. So to build computationally intelligent models for different real world events, researchers have presented numerous types of mathematical formulations of various logic.
Fuzzy logic is one of the dominant logics in the domain of Computational Intelligence. It gives suitable representation of real‐world data/information and enables reasoning that is approximate in nature. It is quite uncommon that the inputs captured by the fuzzy models are 100% complete and determinate. Humans can make intelligent decisions in such situations, though this knowledge is also difficult to express in precise terms. However, an imprecise linguistic description of the manner of control can usually be articulated by the operator with relative ease. In contrast to this, fuzzy models require complete information. Incompleteness and indeterminacy in the data can arise from inherent non‐linearity, the time‐varying nature of the process to be controlled, large unpredictable environmental disturbances, degrading sensors, or other difficulties in obtaining precise and reliable measurements.
This concept is presented with the aim of adding another dimension to the field of Computational Intelligence by discussing relatively a nascent logic: Neutrosophic Logic, proposed by Florentine Smarandache in 1999. Neutrosophic Logic was developed to represent a mathematical model of uncertainty, vagueness, ambiguity, imprecision, incompleteness, inconsistency, redundancy and contradiction. Neutrosophic logic is an extended and general framework for measuring the truth, indeterminacy and falsehood‐ness of the information that closely resembles human psychological behavior. It is effective in representing different attributes of information like inaccuracy, incompleteness and ambiguousness, thus giving a fair estimate of the reliability of information. This concept suggests extending the capabilities of a fuzzy representation and reasoning system by introducing Neutrosophic representation of the data and a Neutrosophic reasoning system.
Neutrosophic logic is a generalized logic that has the capability of encompassing other logics as its special instances. So there seems a possibility of potential conversion of logic specific systems to more generalized and indiscriminate logical systems that can exhibit different logical behaviors depending on the nature of the problem being solved, which is possible by integrating Neutrosophic logic in computationally intelligent models.
Considering the above stated facts, Neutrosophic logic definitely holds a chance to be experimented and utilized for real world executions and human psychology simulations. Neutrosophic logic can find application in areas like web intelligence, medical informatics, bioinformatics, decision making, relational databases, image processing, pattern recognition, preference structures, expert systems and any such domain where the information is very commonly inconsistent, incomplete, uncertain and imprecise.
In this webinar, Swati Aggarwal discusses Neutrosophic Logic in the context of Fuzzy Logic, and how Neutrosophic Logic might be utilized for real world applications and human psychology simulations