Fuzzy Logic and Probabilistic Reasoning Engine

When it comes to ‘true’ or ‘false’, we often think up using 1 and 0 to represent a result. The result is either 1 or 0 because there are only two options. In some cases, however, the truth value variables may be any real number between 0 and 1 inclusive. It is possible to handle concept of partial truth, that is the basic theory of fuzzy logic. Another example can be given as many adjectives like ‘tall’, ‘strong’, etc. President Trump is tall, you will not doubt that when you see his children standing by him, but you won’t feel he is tall any more when comparing Trump with basketball player Yao Ming. So, how tall is ‘tall’? There is no specific definition of these description, and this is fuzzy logic.

Fuzzy logic concept was raised with proposal of fuzzy set theory in 1965, earlier studied as infinite-valued logic dating back to 1920s. Nowadays fuzzy logic models are mathematical means of representing vagueness and imprecise information, focusing on human’s decision from imprecise and non-numerical information, hence the models have capability to recognize, represent, manipulate, interpret and utilize data and information that are vague and lack certainty.

Although both degree of truth and probabilities range between 0 and 1, they different as fuzzy logic uses degrees of truth as a mathematical model of vagueness, while probability is a mathematical model of ignorance. They are widely used in IT, as reasoning system, a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction.

Many reasoning system implement imprecise and semi-formal approximations to recognized logic systems. The systems typically support a variety of procedural and semi-declarative techniques in order that different reasoning strategies can be modeled. These reasoning model emphasize pragmatism over formality and depend on custom extensions and attachments in order to solve real-world problems.

Reasoning system employ variant methods, for example deductive reason, i.e. to draw inferences from available knowledge, and closed world assumption or open world assumption. Among all kinds of reasoning system, they are basically two modes: interactive and batch processing. Interactive systems interface with the user to ask clarifying questions or otherwise allow the suer to guide the reasoning process. Batch system take in all the available information at once and generate the best answer possible without user feedback or guidance.

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