One of the most practical and widely implemented applications of artificial intelligence in business is the development of expert systems and other knowledge-based information systems. A knowledge based information system (KBIS) adds a knowledge base to the major components found in other types of computer based information systems. An expert system (ES) is a knowledge-based information system that uses its knowledge about a specific, complex application area to act as expert consultant to its users. Expert systems provide answers to questions in a very specific problem area making human like inferences about knowledge contained in a specialized knowledge base. They must also be able to explain their reasoning process and conclusions to a user, so expert systems can provide decision support to end users in the form of advice from an expert consultant in a specific problem area.
Benefits of Expert Systems
An expert system captures the expertise of an expert or group of experts in a computer-based information system. Thus, it can outperform a single human expert in many problem situations. That’s because an expert system is faster and more consistent, can have the knowledge of several experts, and does not get tired or distracted overwork or stress. Expert systems also help preserve and reproduce the knowledge of experts. They allow a company to preserve the expertise of an expert before she/he leaves the organization. This expertise can then be shared reproducing the software and knowledge base of the expert system.
Limitations of Expert Systems
The major limitations of expert systems arise from their limited focus, inability to learn, maintenance and developmental cost. Expert systems excel only in solving specific types of problems in a limited domain of knowledge. They fail miserably in solving problems requiring a broad knowledge base and subjective problem solving. They do well with specific types of operational or analytical tasks but falter at subjective managerial decision making.
Expert systems may also be difficult and costly to develop and maintain. The costs of knowledge engineers, lost expert time, and hardware and software resources may be too high to offset the benefits expected from some applications. Also, expert systems can’t maintain themselves; that is, they can’t learn from experience but instead must be taught new knowledge and modified as new expertise is needed to match developments in their subject areas.
Although there are practical applications for expert systems, applications have been limited and specific because, as discussed, expert systems are narrow in their domain of knowledge. An amusing example of this is the user who used an expert system designed to diagnose skin diseases to conclude that his rusty old car had likely developed measles. Additionally, once some of the novelty had worn off, most programmers and developers realized that common expert systems were just more elaborate versions of the same decision logic used in most computer programs. Today, many of the techniques used to develop expert systems can now be found in most complex programs without any fuss about them.