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Computational intelligence

From Wikipedia, the free encyclopedia
For the journal, see Computational Intelligence (journal).
For broader coverage of this topic, see Artificial 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 to associate.[3] Nature-analog or at least nature-inspired methods play a key role in this.[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]

Relationship between hard and soft computing and artificial and computational intelligence

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Artificial intelligence (AI) is used in the media, but also by some of the scientists involved, as a kind of umbrella term for the various techniques associated with it or with CI[5][18]. Craenen and Eiben state that attempts to define or at least describe CI can usually be assigned to one or more of the following groups:

  • Relative definition” comparing CI to AI
  • Conceptual treatment of key notions and their roles in CI
  • Listing of the (established) areas that belong to it[8]
Relationship between hard computing and artificial intelligence on the one hand and soft computing and computational intelligence on the other.[6]

The relationship between CI and AI has been a frequently discussed topic during the development of CI. While the above list implies that they are synonyms, the vast majority of AI/CI researchers working on the subject consider them to be distinct fields, where either[8][18]

  • CI is an alternative to AI
  • AI includes CI
  • CI includes AI

The view of the first of the above three points goes back to Zadeh, the founder of the fuzzy set theory, who differentiated machine intelligence into hard and soft computing techniques, which are used in artificial intelligence on the one hand and computational intelligence on the other.[19][20] In hard computing (HC) and AI, inaccuracy and uncertainty are undesirable characteristics of a system, while soft computing (SC) and thus CI focus on dealing with these characteristics.[14] The adjacent figure illustrates these relationships and lists the most important CI techniques.[6] Another frequently mentioned distinguishing feature is the representation of information in symbolic form in AI and in sub-symbolic form in CI techniques.[17][21]

Hard computing is a conventional computing method based on the principles of certainty and accuracy and it is deterministic. It requires a precisely stated analytical model of the task to be processed and a prewritten program, i.e. a fixed set of instructions. The models used are based on Boolean logic (also called crisp logic[22]), where e.g. an element can be either a member of a set or not. When applied to real-world tasks, systems based on HC result in specific control actions defined by a mathematical model or algorithm. If an unforeseen situation occurs that is not included in the model or algorithm used, the action will most likely fail.[23][24][25][26]

Soft computing, on the other hand, is based on the fact that the human mind is capable of storing information and processing it in a goal-oriented way, even if it is imprecise and lacks certainty.[20] SC is based on the model of the human brain with probabilistic thinking, fuzzy logic and multi-valued logic. Soft computing can process a wealth of data and perform a large number of computations, which may not be exact, in parallel. For hard problems for which no satisfying exact solutions based on HC are available, SC methods can be applied succesfully. SC methods are ususally stochastic in nature i.e., they are a randomly defined processes that can be analyzed statistically but not with precision. Up to now, the results of some CI methods, such as deep learning, cannot be verified and it is also not clear what they are based on. This problem represents an important scientific issue for the future.[23][24][25][26]

AI and CI are catchy terms,[18] but they are also so similar that they can be confused. The meaning of both terms has developed and changed over a long period of time,[27][28] with AI being used first.[3][9] Bezdek describes this impressively and concludes that such buzzwords are frequently used and hyped by the scientific community, science management and (science) journalism.[18] Not least because AI and biological intelligence are emotionally charged terms[3][18] and it is still difficult to find a generally accepted definition for the basic term intelligence.[3][10]

History

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In 1950, Alan Turing, one of the founding fathers of computer science, developed a test for computer intelligence known as the Turing test.[29] In this test, a person can ask questions via a keyboard and a monitor without knowing whether his counterpart is a human or a computer. A computer is considered intelligent if the interrogator cannot distinguish the computer from a human. This illustrates the discussion about intelligent computers at the beginning of the computer age.

The term Computational Intelligence was first used as the title of the journal of the same name in 1985[30][31] and later by the IEEE Neural Networks Council (NNC), which was founded 1989 by a group of researchers interested in the development of biological and artificial neural networks.[32] On November 21, 2001, the NNC became the IEEE Neural Networks Society, to become the IEEE Computational Intelligence Society two years later by including new areas of interest such as fuzzy systems and evolutionary computation.

The NNC helped organize the first IEEE World Congress on Computational Intelligence in Orlando, Florida in 1994.[32] On this conference the first clear definition of Computational Intelligence was introduced by Bezdek: A system is computationally intelligent when it: deals with only numerical (low-level) data, has pattern-recognition components, does not use knowledge in the AI sense; and additionally when it (begins to) exhibit (1) computational adaptivity; (2) computational fault tolerance; (3) speed approaching human-like turnaround and (4) error rates that approximate human performance.[33]

Today, with machine learning and deep learning in particular utilizing a breadth of supervised, unsupervised, and reinforcement learning approaches, the CI landscape has been greatly enhanced, with novell intelligent approaches.

The five main algorithmic approaches of CI and their applications

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

Fuzzy logic

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As explained before, fuzzy logic, one of CI's main principles, consists in measurements and process modelling made for real life's complex processes.[34] It can face incompleteness, and most importantly ignorance of data in a process model, contrarily to Artificial Intelligence, which requires exact knowledge.

This technique tends to apply to a wide range of domains such as control, image processing and decision making. But it is also well introduced in the field of household appliances with washing machines, microwave ovens, etc. We can face it too when using a video camera, where it helps stabilizing the image while holding the camera unsteadily. Other areas such as medical diagnostics, foreign exchange trading and business strategy selection are apart from this principle's numbers of applications.[35]

Fuzzy logic is mainly useful for approximate reasoning, and doesn't have learning abilities,[35] a qualification much needed that human beings have.[citation needed] It enables them to improve themselves by learning from their previous mistakes.

Neural networks

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This is why CI experts work on the development of artificial neural networks based on the biological ones, which can be defined by 3 main components: the cell-body which processes the information, the axon, which is a device enabling the signal conducting, and the synapse, which controls signals. Therefore, artificial neural networks are doted of distributed information processing systems,[36] 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.[37]

Concerning its applications, neural networks can be classified into five groups: data analysis and classification, associative memory, clustering generation of patterns and control.[37] Generally, this method aims to analyze and classify medical data, proceed to face and fraud detection, and most importantly deal with nonlinearities of a system in order to control it.[38] Furthermore, neural networks techniques share with the fuzzy logic ones the advantage of enabling data clustering.

Evolutionary computation

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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.[39] These include the genetic algorithms, evolution strategy, genetic programming and many others.[40] They are considered as problem solvers for tasks not solvable by traditional mathematical methods[41] and are frequently used for optimization including multi-objective optimization.[42]

Learning theory

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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).[43] Learning theories then helps understanding how these effects and experiences are processed, and then helps making predictions based on previous experience.[44]

Probabilistic methods

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Being one of the main elements of fuzzy logic, probabilistic methods firstly introduced by Paul Erdos and Joel Spencer (1974),[45] aim to evaluate the outcomes of a Computation Intelligent system, mostly defined by randomness.[46] Therefore, probabilistic methods bring out the possible solutions to a problem, based on prior knowledge.

Impact on university education

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According to bibliometrics studies, computational intelligence plays a key role in research.[47] 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.[48] 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.[49] 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.[50] There are some efforts available in which multidisciplinary approaches are preferred which allows the student to understand complex adaptive systems.[51] These objectives are discussed only on a theoretical basis. The curriculum of real universities wasn't adapted yet.

Publications

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See also

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Notes

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  • Computational Intelligence: An Introduction by Andries Engelbrecht. Wiley & Sons. ISBN 0-470-84870-7
  • Computational Intelligence: A Logical Approach by David Poole, Alan Mackworth, Randy Goebel. Oxford University Press. ISBN 0-19-510270-3
  • Computational Intelligence: A Methodological Introduction by Kruse, Borgelt, Klawonn, Moewes, Steinbrecher, Held, 2013, Springer, ISBN 9781447150121

References

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