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Expert system - Individuals involved with expert systems | | Expert system - Individuals involved with expert systems: Encyclopedia II - Expert system - Individuals involved with expert systems | | There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds the knowledge base, and a knowledge engineer who assists the experts in determining the representation of their knowledge and who defines the inference technique required to obtain useful problem so ...
See also:Expert system, Expert system - Types of problems solved by expert systems, Expert system - Application, Expert system - Expert systems versus problem-solving systems, Expert system - Individuals involved with expert systems, Expert system - The end user, Expert system - The knowledge engineer, Expert system - The inference rule, Expert system - Chaining, Expert system - Confidences, Expert system - The user interface, Expert system - Procedure node interface, Expert system - How it works, Expert system - Prominent expert systems, Expert system - Bibliography | | | Expert system, Expert system - Application, Expert system - Bibliography, Expert system - Chaining, Expert system - Confidences, Expert system - Expert systems versus problem-solving systems, Expert system - How it works, Expert system - Individuals involved with expert systems, Expert system - Procedure node interface, Expert system - Prominent expert systems, Expert system - The end user, Expert system - The inference rule, Expert system - The knowledge engineer, Expert system - The user interface, Expert system - Types of problems solved by expert systems, Artificial intelligence, Artificial neural network, Fuzzy logic, Heuristic (computer science), Machine learning, Clinical decision support system, Connectionist expert system | | |
| | | Expert system: Encyclopedia II - Expert system - Individuals involved with expert systems
Expert system - Individuals involved with expert systems
There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds the knowledge base, and a knowledge engineer who assists the experts in determining the representation of their knowledge and who defines the inference technique required to obtain useful problem solving activity.
Expert system - The end user
The end-user usually sees an expert system through an interactive dialog, an example of which follows:
Q. Do you know to which restaurant you want to go?
A. No
Q. Is there any kind of food you would particularly like?
A. Unknown
Q. Do you like spicy food?
A. No
Q. Do you usually drink wine with meals?
A. Yes
Q. When you drink wine, is it French wine?
A. Why
As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. This dialog begins with the system asking if the user already knows the restaurant choice (a common feature of expert systems) and immediately illustrates a characteristic of expert systems; users may choose not to respond to any question. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular questions does not stop the consultation.
Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with the question "why", as occurred in the above example. The answer is:
A. I am trying to determine the type of restaurant to suggest. So far Chinese is not a likely choice. It is possible that French is a likely choice. I know that if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French.
It is very difficult to implement a general explanation system (answering questions like Why and How) in traditional systems. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. It is the orderly processing of these rules that dictates the dialog itself.
Expert system - The knowledge engineer
Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. There are several characteristics known to be appropriate to a good inference technique.
1. A good inference technique is independent of the problem domain.
In order to realize the benefits of explanation, knowledge transparency, and reusability of the programs in a new problem domain, the inference engine must not contain domain specific expertise.
2. Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique.
3. Inference techniques are always specific to the knowledge structures.
4. Successful examples of rule processing techniques include:
(a) Forward chaining
(b) Backward chaining
Other related archives1970s, 1980s, AI, Artificial intelligence, Artificial neural network, Backward chaining, CLIPS, Clinical decision support system, Connectionist expert system, Dendral, Earl Weaver, Earl Weaver Baseball, Forward chaining, Fuzzy logic, Heuristic (computer science), How, Jess, Knowledge engineers, Machine learning, Mycin, Problem solving, Prolog, Tony La Russa, Tony La Russa Baseball, Why, accuracy, algorithm, analysis, answer, applications, architecture, artificial intelligence, assertion, attributes, baseball, components, computer, computer games, computer programs, conclusion, conclusions, confidences, consequent, constraints, contents, control structure, course, data, data structures, database, declarations, description, diagnosis, dialog, display, distinction, end-user, entity, errors, evaluation, evidence, expert, expertise, explanation, fuzzy logic, generalization, goal, health, humans, inference engine, inference rule, information, input, interactive, knowledge, knowledge base, knowledge engineer, logic, machine, managers, memory, nature, operator, organization, organizations, personal computer, personal computers, philosophy, probabilities, problem domain, problem domain expert, problem solving, problems, procedure, procedure call, procedures, professional, programmers, programs, question, questions, reasoning, relationships, representation, researcher, researchers, responses, reusability, rule, rules, rules of thumb, statement, task, tax advice, technique, test cases, tree, true, user interface, values, why, wizard (software)
 Adapted from the Wikipedia article "Individuals involved with expert systems", under the G.N U Free Docmentation License. Please also see http://en.wikipedia.org/wiki |
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