Commonly called fuzzy logic, dynamic logic is a system by which value states may contain the added property of degrees in relation to other known or unknown values. Unlike binary logic[?], which only allows for a base 2 system for constructing data, fuzzy logic establishes a matrix relationship between known factors.
As an analogy, in nature there are far more shades of gray than black or white. These represent an important concept that says there are values not black nor white. Taking this further, just like in the Platonic ideal, the binary model doesn't really exist in nature. Everything is colored to some degree by the mere presence of the other.
The exception to this is the need for the analogy of direct opposites, which is what binary logic is essential for. Dynamic logic attempts to transcend the limitations of a binary system by allowing distinct values to have the possibility of degrees, while still maintaining the necessary 'switch' for performing computing functions.
The concept of "degrees of belief" in dynamic logic is similar to that found in the Bayesian school of statistics.
See Fuzzy control system for an example of dynamic logic in use.
The study of neurons in the brain, has yielded very interesting results in confirming comparisons of human intelligence with dynamic logic. Natural intelligence is not binary. Even very uncomplex animals have the capacity to deal with vastly different degrees of information, and process it into the existing state of mind.
Dynamic logic looks to the neuron as a model for creating vastly more variable data processes. The goal is to usefully emulate the power of human intelligence in a machine.
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