CT: Computational intelligence is the study of the design of intelligent agents. An agent is something that acts in an environment—it does something. Agents include worms, dogs, thermostats, airplanes, humans, organizations, and society. An intelligent agent is a system that acts intelligently: What it does is appropriate for its circumstances and its goal, it is flexible to changing environments and changing goals, it learns from experience, and it makes appropriate choices given perceptual limitations and finite computation. The central scientific goal of computational intelligence is to understand the principles that make intelligent behavior possible, in natural or artificial systems. The main hypothesis is that reasoning is computation. The central engineering goal is to specify methods for the design of useful, intelligent artifacts.
S: UBC – https://www.cs.ubc.ca/~poole/ci/ch1.pdf (last access: 24 January 2021)
N: 1. – computational (adj): “pertaining to or of the nature of a computation,” 1857, from computation + -al.
– intelligence (n): late 14c., “faculty of understanding,” from Old French intelligence (12c.), from Latin intelligentia, intellegentia “understanding, power of discerning; art, skill, taste,” from intelligentem (nominative intelligens) “discerning,” present participle of intelligere “to understand, comprehend,” from inter- “between” + legere “choose, pick out, read”).
Meaning superior understanding, sagacity” is from early 15c. Sense of “information, news” first recorded mid-15c., especially “secret information from spies” (1580s). Intelligence quotient first recorded 1921.
. Abbreviation: CI.
2. Computational Intelligence (CI) is the theory, design, application and development of biologically and linguistically motivated computational paradigms.
3. Traditionally the three main pillars of CI have been:
– Neural Networks. Using the human brain as a source of inspiration, artificial neural networks (NNs) are massively parallel distributed networks that have the ability to learn and generalize from examples.
– Fuzzy Systems. Using the human language as a source of inspiration, fuzzy systems (FS) model linguistic imprecision and solve uncertain problems based on a generalization of traditional logic, which enables us to perform approximate reasoning.
– Evolutionary Computation. Using the biological evolution as a source of inspiration, evolutionary computation (EC) solves optimization problems by generating, evaluating and modifying a population of possible solutions.
S: 1. OED – https://www.etymonline.com/search?q=computational+intelligence (last access: 21 January 2021). 2&3. CIS – https://cis.ieee.org/about/what-is-ci (last access: 22 January 2021).
SYN: computational intellect (depending on context)
S: TERMIUM PLUS – https://www.btb.termiumplus.gc.ca/tpv2alpha/alpha-eng.html?lang=eng&i=1&srchtxt=COMPUTATIONAL+INTELLIGENCE&codom2nd_wet=1#resultrecs (last access: 24 January 2021)
CR: android, artificial intelligence, artificial life, automatic control engineering, autopoiesis, black box, big data, blockchain, cognition, cognitive science, computer science, deep learning, drone, expert system, genetic algorithm, intelligent agent, intelligent system, machine learning, ontology, robot , robotics, semantic network, Semantic Web, serendipity, spellchecker, technological singularity, Turing test, virtual personal assistant, virtual reality.