Linux - Artificial Intelligence Concepts and Methodologies - Tools and Applications - Key Terms and Definitions ~ Samuel F. Campbell

Linux - KEY TERMS AND DEFINITIONS

Agent-Based Modelling and Simulation (ABMS):

Image result for agent-based modeling and simulation abms examples

Agent-based modelling and simulation (ABMS) is a practice tool which used to capture the behavioural characteristics of interconnecting agents and the environment around them.

Ant Colony Optimization (ACO):

Ant colony optimization (ACO) is one of the successful research directions in the SI domain. The main idea is that the self-organizing principles which allow a colony of artificial ants that cooperate to find good solutions.

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Computational Intelligence (CI):

The term "CI" was introduced for the emulation of "intelligent" functions of animal brain by digital electronic computers.

Firefly Algorithm (FA):

The FA is a nature-inspired, optimization algorithm which is based on the social (flashing) behaviour of fireflies, or lighting bugs, in the summer sky in the tropical temperature regions.

Fuzzy Logic (FL):

FL is a technique of mapping an input space to an output space by means of a list of linguistic rules that consist of the if-then statements.

Swarm Intelligence (SI):

Swarm intelligence (SI) is built on the core principles found in various natural systems which is composed of many agents who exploits local communication forms and highly distributed control. Informally, SI is a kind of computational metaphor inspired by different swarm examples such as ants, wasps, honey bees, fish, birds, sheep, wolves, and particles.

Teaching–Learning-Based Optimization (TLBO):

The TLBO approach is a new efficient population based algorithm inspired by the influence of a teacher on the output of learners in a class, which learners first acquire knowledge from a teacher (i.e., teacher phase) and then from classmates (i.e., learner phase).

Artificial Neural Networks:

A machine learning technique that emulates the biological nervous system.

Ensemble Learning:

The machine learning paradigm that combines the individual results of standard techniques to evolve a single improved solution to a problem.

Hidden Neurons:: The processing units in the hidden layer of an Artificial Neural Network model that processes variable inputs to produce target outputs.
Linear Search:: A sequential search for an optimal value in a bounded space.
Permeability:: A petroleum reservoir property that measures the interconnectivity of the pores identified in a reservoir rock sample.
Petroleum Reservoir Characterization:: A discipline in petroleum engineering that is concerned with the estimation of various reservoir properties.
Porosity:: A petroleum reservoir property that measures the amount of pores in a reservoir rock sample.
Randomized Algorithm:: An algorithm that generates a number from a normal distribution within a bounded space such that each number has equal chance of being selected.