Деректерден оқуға қабілетті есептеу интеллектісі

Ability of a computer to learn a specific task from data or experimental observation
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The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence. Generally, computational intelligence is a set of nature inspired computational methodologies and approaches to address complex real world problems to which mathematical or traditional modelling can be useless for a few reasons: the processes might be too complex for mathematical reasoning, it might contain some uncertainties during the process, or the process might simply be stochastic in nature. Indeed, many real life problems cannot be translated into binary language (unique values of 0 and 1) for computers to process it. Computational Intelligence therefore provides solutions for such problems. The methods used are close to the human's way of reasoning, i. e. it uses inexact and incomplete knowledge, and it is able to produce control actions in an adaptive way. CI therefore uses a combination of five main complementary techniques. artificial neural networks which permits the system to learn experiential data by operating like the biological one, evolutionary computing, which is based on the process of natural selection, learning theory, and probabilistic methods which helps dealing with uncertainty imprecision. and artificial immune systems, which can be seen as a part of evolutionary computation, image processing, data mining, natural language processing, and artificial intelligence, which tends to be confused with Computational Intelligence. But although both Computational Intelligence (CI) and Artificial Intelligence (AI) seek similar goals, there's a clear distinction between them. Computational Intelligence is thus a way of performing like human beings. Indeed, the characteristic of "intelligence" is usually attributed to humans. More recently, many products and items also claim to be "intelligent", an attribute which is directly linked to the reasoning and decision making.

The main applications of Computational Intelligence include computer science, engineering, data analysis and bio medicine.

As explained before, fuzzy logic, one of CI's main principles, consists in measurements and process modelling made for real life's complex processes. enabling the process and the learning from experiential data. Working like human beings, fault tolerance is also one of the main assets of this principle. Furthermore, neural networks techniques share with the fuzzy logic ones the advantage of enabling data clustering.

Evolutionary computation can be seen as a family of methods and algorithms for global optimization, which are usually based on a population of candidate solutions. They are inspired by biological evolution and are often summarized as evolutionary algorithms. These include the genetic algorithms, evolution strategy, genetic programming and many others. They are considered as problem solvers for tasks not solvable by traditional mathematical methods and are frequently used for optimization including multi objective optimization.

Still looking for a way of "reasoning" close to the humans' one, learning theory is one of the main approaches of CI. In psychology, learning is the process of bringing together cognitive, emotional and environmental effects and experiences to acquire, enhance or change knowledge, skills, values and world views (Ormrod, 1995; Illeris, 2004).

Being one of the main elements of fuzzy logic, probabilistic methods firstly introduced by Paul Erdos and Joel Spencer Therefore, probabilistic methods bring out the possible solutions to a problem, based on prior knowledge.

According to bibliometrics studies, computational intelligence plays a key role in research. All the major academic publishers are accepting manuscripts in which a combination of Fuzzy logic, neural networks and evolutionary computation is discussed. On the other hand, Computational intelligence isn't available in the university curriculum. The amount of technical universities in which students can attend a course is limited. Only British Columbia, Technical University of Dortmund (involved in the European fuzzy boom) and Georgia Southern University are offering courses from this domain. The reason why major university are ignoring the topic is because they don't have the resources. The existing computer science courses are so complex, that at the end of the semester there is no room for fuzzy logic. Sometimes it is taught as a subproject in existing introduction courses, but in most cases the universities are preferring courses about classical AI concepts based on boolean logic, turing machines and toy problems like blocks world. Since a while with the upraising of STEM education, the situation has changed a bit. There are some efforts available in which multidisciplinary approaches are preferred which allows the student to understand complex adaptive systems. These objectives are discussed only on a theoretical basis. The curriculum of real universities wasn't adapted yet.