Al Applications in the Mining Industry Into the 21st Century

Over the past 15 years, Artificial Intelligence (AI) techniques have shown promise of providing the ability to capture `human intelligence' into a computer program. The sub-fields of Expert Systems and Artificial Neural Networks are finding increased use in industry to perform tasks previously considered the domain of human thinking. But, are these methods just a new type of software much like that of the past or is real magic hidden within the lines of code? Will this field continue to enjoy high growth rates and if so, what developments will we see in the near future? Which algorithms and methodologies will continue to grow? This paper discusses a number of paradigms which can be used to create successful applications. There are certain barriers; some psychological - some linked with knowledge acquisition, that must be overcome or avoided to ensure system performance. Development time also requires careful consideration before embarking on an Al project. The paper focusses on constructing goal-driven systems that transfer high-technology solutions from the research laboratory to the plant floor. Respect for system users is of paramount importance - whether their involvement is to access contained information or to participate in program maintenance as new knowledge becomes available. Examples of successful and unsuccessful AI implementation in Canadian industry and academia provide evidence of the importance of this issue. The paper also examines some current constraints on applying AI in the real-world. Among these problems are automated knowledge acquisition, methods to acquire data during run-time, real-time systems (hardware versus software), explanation and justification features and adaptive/leaning systems.