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Editor's Notes

• #4: User supplies facts or other information to the expert system and receives expert advice or expertise in response Internally, the expert system consists of two main components : the knowledge-base contains the knowledge the inference engine draws conclusions from the knowledge The expert knowledge is specific to a problem domain, i.e. medicine, finance, science, etc. However, within the problem domain there is the knowledge domain of the expert or system, which is a wholly contained subset of the problem domain In the knowledge domain, an expert system reasons or makes inferences in the same way that a human expert would infer the solution of a problem. Expert systems can be used as replacements for human experts, in situations where the problem domain is small and well-defined and the knowledge domain of the system is equivalent. However, they are more widely used as intelligent assistants, i.e. decision support systems
• #11: The user interacts with the system through a user interface which may use menus, natural language or any other style of interaction). Then an inference engine is used to reason with both the expert knowledge (extracted from our friendly expert) and data specific to the particular problem being solved. The expert knowledge will typically be in the form of a set of IF-THEN rules. The case specific data includes both data provided by the user and partial conclusions (along with certainty measures) based on this data. In a simple forward chaining rule-based system the case specific data will be the elements in working memory. Almost all expert systems also have an explanation subsystem, which allows the program to explain its reasoning to the user. Some systems also have a knowledge base editor which help the expert or knowledge engineer to easily update and check the knowledge base. One important feature of expert systems is the way they (usually) separate domain specific knowledge from more general purpose reasoning and representation techniques. The general purpose bit (in the dotted box in the figure) is referred to as an expert system shell. As we see in the figure, the shell will provide the inference engine (and knowledge representation scheme), a user interface, an explanation system and sometimes a knowledge base editor. Given a new kind of problem to solve (say, car design), we can usually find a shell that provides the right sort of support for that problem, so all we need to do is provide the expert knowledge. There are numerous commercial expert system shells, each one appropriate for a slightly different range of problems. (Expert systems work in industry includes both writing expert system shells and writing expert systems using shells.) Using shells to write expert systems generally greatly reduces the cost and time of development (compared with writing the expert system from scratch).
• #12: 1.Increased availability expertise becomes available on any suitable computer hardware, thus the system disseminates expertise more widely 2 .Reduced Cost cost per user of providing expertise is lowered 3. Reduced Danger expert systems can be used in situations that would be hazardous to a human 4. Permanence human experts are impermanent 5. Increased Reliability not subject to human variability can be used to confirm or increase confidence that correct decision has been reached. Not advisable if human expert being assisted by system was the one who designed the system 6. Explanation system can explicitly explain in detail to all interested parties, at all times, the reasoning that leads to a conclusion. This increases confidence in the decision, and a human expert would be unlikely to have the time, or the patience, to do this. 7. Fast Response For some applications, especially real-time systems, the expert system may respond faster and be more available than the human expert. 8. Multiple expertise can include the expertise of several human experts