Key terms

Artificial Intelligence (AI):

The field of study devoted to building machines which exhibit “intelligence,” as commonly understood in relation to humans.

Artificial Neural Networks (ANNs):

Simplified models of the human brain (biological neural network) which are particularly adept at pattern recognition or classification.

Backpropagation (BP) Algorithm:

Used to train Multi-layer Perceptrons (supervised, feedforward neural networks); BP adjusts the weights connecting neurons in the various layers according to the error (or difference) between actual and desired outputs generated in response to presentation of input-output training pattern pairs (exemplars).

Computational Intelligence (CI):

Incorporates ANN, Fuzzy and Evolutionary approaches, and more especially hybrids of these (some authors extend this definition to include intelligent agents, stochastic reasoning and other techniques).

DNA Computing:

The implementation of classical computing algorithms by way of chemical reactions within a test tube (so called “wet computing”).

Evolutionary Algorithms (EAs):

An iterative procedure which involve the “mating” of suitable parents from a population of solutions to a problem of interest, in the hope that more suitable “offspring” (i.e., solutions) will evolve over time.

Expert (or Knowledge-Based) System (ES/KBS):

Comprise a (Graphical) User Interface, an Inference Engine and a Knowledge Base. The GUI accepts user queries (inputs), and presents the results to these queries (outputs) in a comprehensible manner, usually together with some justification (rules) or confidence level.

Immune-Based Computing:

Uses “antigens” to discriminate between “self” and “nonself,” and to affect selfrepair within a system.

Intelligence:

Difficult to define exactly, but incorporates awareness of (and ability to interact with and adapt to) ones environment, as well as an ability to learn from experience (thereby increasing our knowledge).

Intelligent System:

Used in this article primarily to mean (a) biologically-inspired “soft computing” techniques which can be incorporated into an information system (IS) in order to improve performance, and secondarily (b) in the sense of intelligence gathering, in order to render an IS more secure.

Soft Computing:

An older term for Computational Intelligence (see above).

Swarm Intelligence (SI):

Refers to a class of algorithms inspired by the collective behaviour of insect swarms, ant colonies, the flocking behaviour of some bird species, or the herding behaviour of some mammals, such that the behaviour of the whole can be considered as exhibiting a rudimentary form of “intelligence.

Дата добавления: 2015-07-10