Uncertainty appears in different forms and affects decisions making. Frequently, information may be incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, or deficient in some other way [1]. Much of this uncertainty can be handle using Fuzzy logic type 1 [2], Fuzzy logic type 2 [3,4] or Intuitionistic fuzzy logic [5,6]. The theory of fuzzy logic provides a mathematical theory to capture the uncertainties associated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for representing the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic do not provide an appropriate conceptual framework for dealing with the representation of commonsense knowledge, since such knowledge is by its nature both lexically imprecise and non-categorical. The development of fuzzy logic was motivated in large measure by the need for a conceptual framework which can address the issue of lexical imprecision. Intuitionistic fuzzy sets (IFSs), proposed [5–۷] as a generalization of the traditional fuzzy sets introduced by Zadeh [2], have the property to incorporate the uncertainty of the information. The IFSs offer a new possibility to represent imperfect knowledge and, therefore, to describe in a more adequate way many real problems. Such problems appear when we face with human opinions involving two or more answers of the type: “Yes”, “Not”, “I do not know”, “I am not sure”, etc. Many applications have highlighted the superiority of intuitionist fuzzy logic compared to traditional fuzzy logic; in the following we mention some of them. In [8] a new approach for fuzzy inference in intuitionistic fuzzy system applied to monitoring a non-linear dynamic plant were described. In [9] an optimized method to reduce the points number to be used in order to identify a person using fuzzy fingerprints is described. The design of an intuitionistic fuzzy logic controller for heater fans on the basis of intuitionistic fuzzy systems were presented in [10]. In [11] an intuitionistic fuzzy logic controller to determine washing time for a washing machine is developed. Because the conventional traffic controller is unable to solve a set of problems (for instance, the congestion at the intersection) in [12] an intelligent transport system based of intuitionistic fuzzy logic is proposed. In [13] a method to construct type-1 intuitionistic fuzzy inference that is able to handle more uncertainty than type-1 fuzzy inference system and performs faster than a type-2 fuzzy inference system were described. The results show that the intuitionistic fuzzy inference system performs better than other methods. Many works have been dedicated to improving the architectures and theory used in the construction of control systems. For instance, in [14] a new general procedure is proposed to construct the membership and non-membership functions of the fuzzy reliability using time-dependent intuitionistic fuzzy sets and in [15] an approach for graphically representing intuitionistic fuzzy sets for their use in Mamdani fuzzy inference systems were proposed. The importance of intuitionistic fuzzy sets is explained, also, in [16] was proved that any type-2 fuzzy set Aˆ can be represented by intuitionistic fuzzy set A* g . Some questions appear.