Difference between revisions of "Expert system"
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* Medical diagnostics (e.g. "I have a pain in my lower abdomen, what should I do?"); | * Medical diagnostics (e.g. "I have a pain in my lower abdomen, what should I do?"); | ||
* Product selection (e.g. "Which device is optimal for my system?) | * Product selection (e.g. "Which device is optimal for my system?) | ||
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| + | == Expert System Architecture == | ||
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| + | An expert system has 3 components: | ||
| + | |||
| + | * '''The Knowledge Base''' - where the information is stored in the expert system in the form of facts and rules (basically a series of IF statements). This is where the programmer writes the code for the expert system. | ||
| + | |||
| + | * '''The User Interface''' - where the user interacts with the expert system. In other words where questions are asked, and advice is produced. As well as the advice that is output, the user interface can output the justification features of an expert system. This is either How justification - where the system justifies its reasoning for providing a piece of advice or Why justification - where the system justifies why a particular question is being asked. | ||
| + | |||
| + | Justification allows the user piece of mind about why a question is asked or a piece of advice is provided, and can increase their confidence in taking such advice. | ||
| + | It also makes it easier for the programmer of the system to ensure that it works correctly as it will flag up areas where the expert system provides advice that is not intended by the programmer. | ||
| + | |||
| + | * '''Inference Engine''' - this applies the facts to the rules and determines the questions to be asked of the user in the user interface and in which order to ask them. This is the 'invisible' part of the expert system, which is active during a consultaion of the system (when the user chooses to run the program). | ||
| + | |||
| + | An expert system can use 2 different methods of inferencing - '''Forward Chaining''' and '''Backward Chaining'''. | ||
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| + | A '''Backward Chaining system''' (a goal driven system) works with the system assuming a hypothesis of what the likely outcome will be, and the system then works backwards to collect the evidence that would support this conclusion. Expert systems used for planning often use backward chaining. | ||
| + | |||
| + | A '''Forward Chaining expert system''' (a data driven system) simply gathers facts (like a detective at the scene of a crime) until enough evidence is collected that points to an outcome. Forward chaining is often used in expert systems for diagnosis, advise and classification, although the size and complexity of the system can play a part in deciding which method of inferencing to use. | ||
==Related articles== | ==Related articles== | ||
Revision as of 14:39, 9 March 2016
Definition
A data processing system that provides for solving problems in an expert manner within a given field or application area, by drawing inferences with the aid of a knowledge base developed from human expertise. An expert system is a branch of artificial intelligence. See Artificial intelligence and Knowledge base
Description
An expert system is a rule-based system that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain and is a traditional application and/or subfield of artificial intelligence.
Expert systems are most valuable to nuclear organisations which have a high-level of know-how experience and expertise that cannot be easily transferred to other members. They are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem-solving purposes.
There are generally three individuals having an interaction with expert systems. The main one 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 and supplies the knowledge base providing the domain expertise, and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters this knowledge into an explanation module and defines the inference technique required to obtain useful problem solving activity.
Usually, the knowledge engineer will represent the problem solving activity in the form of rules which is referred to as a rule-based expert system. When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system.
What does it mean 'Rule-based system'?
In expert system technology, the knowledge base is expressed with natural language rules IF ... THEN ... For examples : "IF it is living THEN it is mortal" "IF his age = known THEN his year of birth = date of today - his age in years"
This formulation has the advantage of speaking in everyday language which is very rare in computer science (software is coded). Rules express the knowledge to be exploited by the expert system. There exists other formulations of rules, which are not in everyday language, understandable only to computer scientists. Each rule style is adapted to an engine style. The whole problem of expert systems is to collect this knowledge, usually unconscious, from the experts. There are methods but almost all are usable only by computer scientists.
Typical areas of expert system applications:
- Troubleshooting technology issues (e.g. “Equipment was not work correct”);
- Medical diagnostics (e.g. "I have a pain in my lower abdomen, what should I do?");
- Product selection (e.g. "Which device is optimal for my system?)
Expert System Architecture
An expert system has 3 components:
- The Knowledge Base - where the information is stored in the expert system in the form of facts and rules (basically a series of IF statements). This is where the programmer writes the code for the expert system.
- The User Interface - where the user interacts with the expert system. In other words where questions are asked, and advice is produced. As well as the advice that is output, the user interface can output the justification features of an expert system. This is either How justification - where the system justifies its reasoning for providing a piece of advice or Why justification - where the system justifies why a particular question is being asked.
Justification allows the user piece of mind about why a question is asked or a piece of advice is provided, and can increase their confidence in taking such advice. It also makes it easier for the programmer of the system to ensure that it works correctly as it will flag up areas where the expert system provides advice that is not intended by the programmer.
- Inference Engine - this applies the facts to the rules and determines the questions to be asked of the user in the user interface and in which order to ask them. This is the 'invisible' part of the expert system, which is active during a consultaion of the system (when the user chooses to run the program).
An expert system can use 2 different methods of inferencing - Forward Chaining and Backward Chaining.
A Backward Chaining system (a goal driven system) works with the system assuming a hypothesis of what the likely outcome will be, and the system then works backwards to collect the evidence that would support this conclusion. Expert systems used for planning often use backward chaining.
A Forward Chaining expert system (a data driven system) simply gathers facts (like a detective at the scene of a crime) until enough evidence is collected that points to an outcome. Forward chaining is often used in expert systems for diagnosis, advise and classification, although the size and complexity of the system can play a part in deciding which method of inferencing to use.
