ABSTRACT
Since the term was rst coined in 1955 [1], arti cial intelligence (AI) has become a rapidly expanding area of research, popularized by numerous books, both ction and non ction. However, although it may be safe to assume that all readers will be familiar with the term, we need to establish a working de nition for the purpose of setting the context for this chapter. Although several de nitions of AI systems have been proposed [2-4], the question of how to quantify AI remains an open research topic within the AI research community. For the purpose of this review we will use the relatively loose de nition that an arti cially intelligent system (IS) is one that includes a knowledge component and is capable of learning such that it is able to sense and adapt to its environment. Well-known examples of such systems include expert systems, fuzzy systems, arti cial neural networks (ANN), and models of statistical inference such as Bayesian networks.
