The term Fuzzy Logic was first introduced by Zadeh in the early 60's and since then a rigorous mathematical foundation has been built, followed by a vast amount of applications: from intelligent washing machines to sophisticated systems for data analysis and decision support.

Fuzzy logic is a system of concepts, principles and methods for dealing with modes of reasoning that are approximate rather than exact (Klir et al, 1997). Fuzzy set theory was developed to quantify concepts that are vague and imprecise and cannot be described by the crisp two-valued classical logic. It extents the multi-valued logic to allow inferences to include propositions whose truth-values might be partly true and partly false.

The core concept in fuzzy logic is the concept of the fuzzy set. A fuzzy set is a set that does not have sharp boundaries, in the sense that contains elements in different "degrees". The degree of membership in a set is expressed by a number between 0 and 1. A fuzzy set is defined by a membership function, which maps elements in a domain of interest (universe of discourse) to their membership value in the set. The most attractive aspect of fuzzy sets is that they can be associated with linguistic terms that correspond to human expert knowledge and intuition (terms like good, bad, high, low etc).

So, fuzzy logic is a very powerful resource of theories and techniques to deal with multivalent and ambiguous environments -like these of design and planning-, and offers a powerful framework for knowledge representation.