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Two-valued, or Boolean, logic is a well-de ned and used theory Boolean logic is especially important for implementation in computing systems where information, or knowledge about a problem, is binary encoded Boolean logic also played an important role in the development of the rst AI reasoning systems, especially the inference engine of expert systems [315] For such knowledge representation and reasoning systems, propositional and rst-order predicate calculus are extensively used as representation language [539, 629] Associated with Boolean logic is the traditional two-valued set theory, where an element either belongs to a set or not That is, set membership is precise Coupled with Boolean knowledge, two-valued set theory enabled the development of exact reasoning systems While some successes have been achieved using two-valued logic and sets, it is not possible to solve all problems by mapping the domain into two-valued variables Most real-world problems are characterized by the ability of a representation language (or logic) to process incomplete, imprecise, vague or uncertain information While twovalued logic and set theory fail in such environments, fuzzy logic and fuzzy sets give the formal tools to reason about such uncertain information With fuzzy logic, domains are characterized by linguistic terms, rather than by numbers For example, in the phrases it is partly cloudy , or Stephan is very tall , both partly and very are linguistic terms describing the magnitude of the fuzzy (or linguistic) variables cloudy and tall The human brain has the ability to understand these terms, and infer from them that it will most probably not rain, and that Stephan might just be a good basket ball player (note, again, the fuzzy terms!) However, how do we use two-valued logic to represent these phrases Together with fuzzy logic, fuzzy set theory provides the tools to develop software products that model human reasoning (also referred to as approximate reasoning) In fuzzy sets, an element belongs to a set to a degree, indicating the certainty (or uncertainty) of membership The development of logic has a long and rich history, in which major philosophers played a role The foundations of two-valued logic stemmed from the e orts of Aristotle (and other philosophers of that time), resulting in the so-called Laws of Thought [440] The rst version of these laws was proposed around 400 BC, namely the Law of the Excluded Middle This law states that every proposition must have only one of two outcomes: either true or false Even in that time, immediate objections were given with examples of propositions that could be true, and simultaneously not true 451.

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It was another great philosopher, Plato, who laid the foundations of what is today referred to as fuzzy logic It was, however, only in the 1900s that Lejewski and Lukasiewicz [514] proposed the rst alternative to the Aristotelian two-valued logic Three-valued logic has a third value which is assigned a numeric value between true and false Lukasiewicz later extended this to four-valued and ve-valued logic It was only recently, in 1965, that Lot Zadeh [944] produced the foundations of in nite-valued logic with his mathematics of fuzzy set theory Following the work of Zadeh, much research has been done in the theory of fuzzy systems, with applications in control, information systems, pattern recognition and decision support Some successful real-world applications include automatic control of dam gates for hydroelectric-powerplants, camera aiming, compensation against vibrations in camcorders, cruise-control for automobiles, controlling air-conditioning systems, document archiving systems, optimized planning of bus time-tables, and many more While fuzzy sets and logic have been used to solve real-world problems, they were also combined with other CI paradigms to form hybrid systems, for example, fuzzy neural networks and fuzzy genetic algorithms [957] A di erent set theoretic approach which also uses the concept of membership functions, namely rough sets (introduced by Pawlak in 1982 [668]), is sometimes confused with fuzzy sets While both fuzzy sets and rough sets make use of membership functions, rough sets di er in the sense that a lower and upper approximation to the rough set is determined The lower approximation consists of all elements that belong with full certainty to the corresponding set, while the upper approximation consists of elements that may possibly belong to the set Rough sets are frequently used in machine learning as classi er, where they are used to nd the smallest number of features to discern between classes [600] Rough sets are also used for extracting knowledge from incomplete data [600, 683] Hybrid approaches that employ both fuzzy and rough sets have also been developed [843] The remainder of this Part is organized as follows: 20 discusses fuzzy sets, while fuzzy logic and reasoning are covered in 21 A short overview of fuzzy controllers is given in 22 The Part is concluded with an overview of rough set theory in 23.

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