Artificial Intelligence and Expert Systems for Engineers
Introduction
Engineer utilise principles of science and mathematics to develop certain technologies. These technologies are then used to create engineered artifacts such as products, structures, machines, processes or entire systems. However, this is too abstract a definition for the engineer’s sphere of operation. It must be analysed in greater detail for an understanding of how engineers create the artifacts that improve the quality of life. When an engineer creates an artifact in any area of application, he has to employ a host of related activities like planning, conceptual design, analysis, detailing, drafting, construction, manufacturing and maintenance. Depending on the type of problem that is being addressed and the domain, different combinations and different sequences of these activities are undertaken. Right from the days of ENIAC, the first digital computer, computers have been extensively used by the engineering community to expedite or automate some of the numerous tasks. The history of the use of computer in engineering problems parallels the developments in computer hardware and software technology. Such developments have advanced at such an unbelievable pace in the past fifteen years that today’s desktop computers are far more capable than the mainframe computers of the last decade. Developments are not constrained to faster CPUs alone. The emergence of improved paradigms such as parallel and distributed computing, backed up by appropriate software environments, has virtually transformed the direction of research in computer usage in engineering. From the development of faster and faster algorithms, we have moved to developments for evolving improved methods of assistance. This has resulted in the transformation of computers from large numerical computing machines to aids to engineers at every stage of problem solving. Numerical computing-intensive tasks were the early applications attempted to be solved with the aid of computers in the early days of computer usage by the engineering community. Research in
the areas of computer graphics, database management systems and Artificial Intelligence (AI) along with the development of faster and more powerful hardware platforms accelerated and widened the use of computers for engineering problem solving. Computer graphics tools improved the visualisation capabilities, thereby making it possible for complete graphical simulation of many engineering processes. DataBase Management Systems (DBMS) provided engineers with necessary tools for handling and manipulating the large amount of data generated during processing in a systematic and efficient manner. Integration of spatial information handling and graphical presentation with DBMS provided a very powerful tool, viz., the Geographical Information System (GIS), which has really revolutionised computer-assisted execution of many tasks in many disciplines of engineering. Still, all these developments helped only numerical computing-intensive, data-intensive and visualisation-based problems. One of the major tasks in many of the activities mentioned earlier is decision making, which is required in different stages of execution of each of the tasks. Decision making requires processing of symbolic information in contrast to the conventional data processing, handling of facts and inference using domain knowledge. Inference is nothing but search through the knowledge base using the facts. The intensive research carried out in the area of AI in the last four decades resulted in the emergence of a number of useful techniques which can be used for solving many complex
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