Computational intelligence

In computer science, computational intelligence (CI) refers to concepts, paradigms, algorithms and implementations of systems that are designed to show "intelligent" behavior in complex and changing environments.^[1] These systems are aimed at mastering complex tasks in a wide variety of technical or commercial areas and offer solutions that recognize and interpret patterns, control processes, support decision-making or autonomously manoeuvre vehicles or robots in unknown environments, among other things.^[2] These concepts and paradigms are characterized by the ability to learn or adapt to new situations, to generalize, to abstract, to discover and associate.^[3] Nature-analog or nature-inspired methods play a key role, such as in neuroevolution for Computational Intelligence.^[1]

CI approaches primarily address those complex real-world problems for which mathematical or traditional modeling is not appropriate for various reasons: the processes cannot be described exactly with complete knowledge, the processes are too complex for mathematical reasoning, they contain some uncertainties during the process, such as unforeseen changes in the environment or in the process itself, or the processes are simply stochastic in nature. Thus, CI techniques are properly aimed at processes that are ill-defined, complex, nonlinear, time-varying and/or stochastic.^[4]

A recent definition of the IEEE Computational Intelligence Societey describes CI as the theory, design, application and development of biologically and linguistically motivated computational paradigms. Traditionally the three main pillars of CI have been Neural Networks, Fuzzy Systems and Evolutionary Computation. ... CI is an evolving field and at present in addition to the three main constituents, it encompasses computing paradigms like ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. ... Over the last few years there has been an explosion of research on Deep Learning, in particular deep convolutional neural networks. Nowadays, deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems are based on CI.^[5] However, as CI is an emerging and developing field there is no final definition of CI,^[6][7][8] especially in terms of the list of concepts and paradigms that belong to it.^[3][9][10]

The general requirements for the development of an “intelligent system” are ultimately always the same, namely the simulation of intelligent thinking and action in a specific area of application. To do this, the knowledge about this area must be represented in a model so that it can be processed. The quality of the resulting system depends largely on how well the model was chosen in the development process. Sometimes data-driven methods are suitable for finding a good model and sometimes logic-based knowledge representations deliver better results. Hybrid models are usually used in real applications.^[2]

According to actual textbooks, the following methods and paradigms, which largely complement each other, can be regarded as parts of CI:^{[11][12][13][14][15][16][17]}

• Fuzzy systems^{[11][12][13][14][15][16][17]}
• Neural networks^{[11][12][14][15]} and, in particular, convolutional neural networks^[13][16][17]
• Evolutionary computation^[14][15] and, in particular, multi-objective evolutionary optimization^{[11][12][13][16][17]}
• Swarm intelligence^{[11][12][13][14][15][16][17]}
• Bayesian networks^[13][16][17]
• Artificial immune systems^[15][17]
• Learning theory^[12]
• Probabilistic Methods^[12]