An Introductory Review - Expert Systems in Mining Engineering

In a 1985 seminar, we were told that overoptimism abounds in Artificial Intelligence (AI), that A1 is neither a science nor a technology yet, and that we will be both exhilarated and disappointed with AI. In fact, even today, there is no single accepted definition of AI; however, certain features have been identified by AI researchers as essential to consider an entity.(e.g: machine or program) to be intelligent. According to Shank (1987), these features include communication (to be able to communicate with), internal knowledge (knowledge about itself), world knowledge (awareness and knowledge about the world outside), intentionality (knowing what one wants and a plan to seek it, ie. goal seeking behavior), and creativity (being adaptable to changing conditions). None of these figures would individually define intelligence; however, each would be a part of intelligence in its own way. The prospects for the development of entities with AI have never been as good as they are now. The combination of advancements in computer hardware and software along with new insights into the ways in which we store knowledge and use it for problem solving has enhanced the potential for the creation of intelligent entities. After some unbounded optimism of a major breakthrough in the fifties and frustrating disappointments in the two following decades, it appears now that AI has emerged from research labs to industrial practice. This is confined by the growing number of repeat applications of AI in business and industry to the extent that AI can now be considered a technology.