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An expert system is a class of computer programs developed by researchers in artificial intelligence during the 1970s and applied
commercially throughout the 1980s. In essence, they are programs made up of a set of rules that analyze information (usually supplied
by the user of the system) about a specific class of problems, as well as provide analysis of the problem(s), and, depending upon
their design, a recommended course of user action in order to implement corrections.
Type of problems solved by expert systems
Typically, the problems to be solved are of the sort that would normally be tackled by a human "expert" - a medical or other professional,
in most cases. Real experts in the problem domain (which will typically be very narrow, for instance "diagnosing skin diseases in human
teenagers") are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced system
developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data
and (in a strictly limited manner) derive rules from that.
Simple systems use simple true/false logic to evaluate data, but more sophisticated systems are capable of performing at least some evaluation
taking into account real-world uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly
imperfect.
Application
While expert systems have distinguished themselves in AI research in finding practical application, their application has been limited.
Expert systems are notoriously narrow in their domain of knowledge-as an amusing example, a researcher used the "skin disease" expert system
to diagnose his rustbucket car as likely to have developed measles-and thus prone to making errors that humans would easily spot. Additionally,
once some of the mystique had worn off, most programmers realized that simple expert systems were essentially just slightly more elaborate
versions of the decision logic they had already been using. Therefore, some of the techniques of expert systems can now be found in most
complex programs without any fuss about them.
Benefits of Expert Systems
- Increased output & productivity - Expert systems can work faster than humans and that means fewer workers are needed. Therefore, it reduced the costs and increased the output.
- Reduced downtime - Expert systems can save a considerable amount of money for the company involved by reducing the downtime.
- Increased quality - Expert systems provides consistent advice and reduces the rate of error.
- Capture of scarce expertise - The scarcity of expertise becomes evident in situations where the expert is retiring or leaving the job.
- Reliability - Expert systems are reliable as they do not take medical leave, go on strike, or get tired.
- Accessibility to knowledge & help desks - Expert systems make knowledge accessible to people who query the systems for advice.
- Flexibility
- Increased capabilities of other computerized systems
Related Topics
Organizational learning
Business Intelligence
Business Intelligence - Metrics /Key Performance Indicators
Business performance management
What is Knowledge Representation
Data warehouse
Data mining
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