Expert systems, computer-based

Expert systems are based on spectral feature-substructure relationship rules that comprise the knowledge base for IR spectral analysis. This is the main difference from neural network techniques, where no prior knowledge about the structure-spectrum relationship is necessary because the network learns inductively from examples. For expert systems a knowledge base has to be established and transformed into a computer operable form. This expert knowledge is expressed in terms of substructure-subspectra relationships. 

Rl accomplishes this task correctiy in a fraction of the time it would take for human technicians. The savings in this case are in terms of the number of orders processed, which ultimately translates to doUars. As a final example, consider Prospector, another classic expert system built in the 1970s (4). This computer program, designed to detect commercially viable ore deposits based on geological data, correctiy identified a molybdenum ore deposit worth about 100 million. 

The need for rapidly accessible estimation of toxicity has led to the development of software and other algorithms that will generate estimations of toxicity, usually for organic compounds [79] such methodology is termed an expert system, which has been defined [34] as any formalised system, not necessarily computer-based, which enables a user to obtain rational predictions about the toxicity of chemicals. Essentially, expert systems fall into two classes— those relying on statistical approaches and those based on explicit rules derived from human knowledge. 

An expert system is a computer program that draws upon the knowledge of human experts captured in a knowledge base to solve problems that normally require human expertise. [2],... 

Case-based reasoning is very much dependent on the structure and content of its cases and their representation because case retrieval involves identifying those features in the problem that best match those in the case base. The dynamic addition of new cases means that CBR is intrinsically a learning methodology such that the performance of an expert system based on this approach will improve with time [9]. Systems may be developed with conventional computer languages or shells [7]. 

Rules seemingly have the same format as IF.. THEN.. statements in any other conventional computer language. The major difference is that the latter statements are constructed to be executed sequentially and always in the same order, whereas expert system rules are meant as little independent pieces of knowledge. It is the task of the inference engine to recognize the applicable rules. This may be different in different situations. There is no preset order in which the rules must be executed. Clarity of the rule base is an essential characteristic because it must be possible to control and follow the system on reasoning errors. The structuring of rules into rule sets favours comprehensibility and allows a more efficient consultation of the system. Because of the natural resemblance to real expertise, rule-based expert systems are the most popular. Many of the earlier developed systems are pure rule-based systems. 

Until recently, most expert system building took place in the research departments of universities and a few major corporations. The primary emphasis was investigation of artificial intelligence principles, and the application was of secondary importance. The expert systems tools used reflect this interest. They are typically stand-alone AI computer systems, using special hardware and software environments (usually Lispr-based) not commonly fo md in scientific and engineering organizations. 

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