Intelligence is a capability of a system to achieve a goal or sustain desired behavior under conditions of uncertainty. Intelligence systems have to cope with the sources of uncertainty like the occurrence of unexpected events-such as an unpredictable changes in the world in which the systems operates, and incomplete, inconsistent or unreliable information available to the system for the purpose of deciding what to do next.

It is important to contrast intelligent systems, i.e., systems that can make decisions under uncertainty, with the systems that are programmed to make only deterministic decisions. Data processing systems, conventional robots, production lines and computer controlled machines tools are examples of such non-intelligent systems.

Intelligent systems exhibit intelligent behavior. Intelligent behavior is exhibited by artifacts and biological systems capable of achieving specified goals or sustaining desired behavior under conditions of uncertainty even in poor structured environments. Such environments are environments in which variable characteristics are not measurable, where several characteristics change simultaneously and in unexpected ways and where it is not possible to decide in advance how the system should respond to every combination of events. Some characteristics of intelligent behavior are: adaptability, learning, goal-seeking, self-improvement and reproduction.

Rzevski [13] and Figueredo, et al. [5] reviews the five paradigms of artificial intelligence in engineering. Such paradigms are; knowledge based systems, neural networks, fuzzy logic, genetic algorithms and intelligent agents.

Genetic algorithm, for example, is a computational equivalent of evolution, of survival of the fittest. Most of the interesting feature of genetic algorithm is their ability to expand the search space, to diverge, as well as to converge. For this reason, GA is quite effective as search algorithm, particularly for solving optimization problems with a large number of local minima. Some application of Genetic Algorithm in engineering are: optimal capacitor placement and control [18,10], economic load dispatch, optimal power flow [2], expansion planning [9], and unit commitment problems [19]. Figueredo, et al. [5] give a comprehensive review of the application of AI in electric power engineering

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* This is a part of the Literature Review for my undergraduate thesis on Optimal Capacitor Allocation. Bibliography can be found HERE