The simplest way to define fuzzy logic is by comparison to binary logic. In the previous chapters, we looked at transition rules as true or false or 0 or 1 values. Is something visible? Is it at least a certain distance away? Even in instances where multiple values were being evaluated, all of the values had exactly two outcomes thus, they are binary. In contrast, fuzzy values represent a much richer range of possibilities, where each value is represented as a float rather than an integer. We stop looking at values as 0 or 1, and we start looking at them as 0 to 1.
A common example used to describe fuzzy logic is temperature. Fuzzy logic allows us to make decisions based on non-specific data. I can step outside on a sunny California summer day and ascertain that it is warm, without knowing the temperature precisely. Conversely, if I were to find myself in Alaska during the winter, I would know that it is cold, again, without knowing the exact temperature. These concepts...