The expert systems have the following components:
(1). Knowledge acquisition facility
(2). Knowledge base
(3). Knowledge-based management system
(4). Reasoning capability
(5). Work space
(6). Explanation facility
(7). Inference engine, and
(8). User interface
These components are briefly explained below:
1. Knowledge Acquisition Facility
Domain experts acquire expertise in their area of expertise over a long period. The
expertise may be the result of their constant interaction with similar experts, observation and
personal experience in the domain. Capturing expertise is one of the most difficult tasks of
building knowledge base. This facility adds new knowledge and rules to the existing knowledge
base and ensures its growth to meet emerging need. Usually a knowledge engineer takes care of
this task. He identifies and interacts with the domain experts to gather expertise.
2. Knowledge Base
Knowledge base is just like the database of information system. It stores knowledge and
rules and explanations associated with the knowledge. Knowledge representation is a major task
in expert system building. The knowledge must be meaningfully represented in the system so
that the system can relate to real world problems.
The knowledge base includes three of knowledge such as:
Factual knowledge
Heuristic knowledge, and
Meta knowledge
The factual knowledge consists of facts about the domain, say, finance, medicine, design,
etc., heuristic knowledge relates to the rules associated with a domain or problem area. Meta
knowledge enables the expert system to use and analyses facts, extract those facts and specify the
route to a solution. It refers to the ability of an expert system to learn from its own experience.
The knowledge base contains data and facts relevant to a problem area. The most
common way to represent knowledge in expert systems is in the form of rules such as if
……then statements. Semantic networks and frames are other forms of knowledge
representation in expert systems. The inference engine contains reasoning methods. It is a piece
of software that probes the user and searches the knowledge base for the appropriate solution.
The user interface links the user with the expert system. It sorts up screens for user-interaction
with the system. Such interaction leads to identification and solution of problems.
3. Knowledge-based Management System
It is similar to a database management system in an information system. Its major task is
to up data the knowledge base with knowledge and rules.
4. Work Space
The workspace or black board is a memory area used for describing the current problem,
and storing intermediate results.
5. Explanation Facility
Most expert systems have explanation facilities. It explains how recommendations are
derived. The user can know how the expert system arrived at the solution, why some alternatives
were rejected, why some information was asked for etc. The explanation facility answers these
questions by referring to the system goals, data input and the decision rules. For example, in
case of loan proposal evaluation, the expert system’s explanation facility will clarify on probing
why one application was approved and why another was rejected. In case of a medical expert
system such as Mycin, this facility builds confidence in the user about the expert system and the
solution it provides to problem.
6.Reasoning Capability or Knowledge Refinement
The expert system has the capability to analyze why its solution failed or succeeded and
ways of improving its solution.
7. Inference Engine
The inference engine works like the model base in decision support system. It
manipulates a series of rules using forward chining and backward chaining techniques. In
forward chaining the inference engine poses a series of if ….then condition checking. Based on
the responses a particular solution is suggested. In backward chaining technique, the inference
engine starts with the goal and checks whether the conditions leading to that goal are present.
8. User Interface
The system provides an interface for the users to interact with the system to generate solutions.
It is similar to the dialogue facility in decision support system. The artificial intelligence
technology tries to provide a natural language interface to users.
(1). Knowledge acquisition facility
(2). Knowledge base
(3). Knowledge-based management system
(4). Reasoning capability
(5). Work space
(6). Explanation facility
(7). Inference engine, and
(8). User interface
These components are briefly explained below:
1. Knowledge Acquisition Facility
Domain experts acquire expertise in their area of expertise over a long period. The
expertise may be the result of their constant interaction with similar experts, observation and
personal experience in the domain. Capturing expertise is one of the most difficult tasks of
building knowledge base. This facility adds new knowledge and rules to the existing knowledge
base and ensures its growth to meet emerging need. Usually a knowledge engineer takes care of
this task. He identifies and interacts with the domain experts to gather expertise.
2. Knowledge Base
Knowledge base is just like the database of information system. It stores knowledge and
rules and explanations associated with the knowledge. Knowledge representation is a major task
in expert system building. The knowledge must be meaningfully represented in the system so
that the system can relate to real world problems.
The knowledge base includes three of knowledge such as:
Factual knowledge
Heuristic knowledge, and
Meta knowledge
The factual knowledge consists of facts about the domain, say, finance, medicine, design,
etc., heuristic knowledge relates to the rules associated with a domain or problem area. Meta
knowledge enables the expert system to use and analyses facts, extract those facts and specify the
route to a solution. It refers to the ability of an expert system to learn from its own experience.
The knowledge base contains data and facts relevant to a problem area. The most
common way to represent knowledge in expert systems is in the form of rules such as if
……then statements. Semantic networks and frames are other forms of knowledge
representation in expert systems. The inference engine contains reasoning methods. It is a piece
of software that probes the user and searches the knowledge base for the appropriate solution.
The user interface links the user with the expert system. It sorts up screens for user-interaction
with the system. Such interaction leads to identification and solution of problems.
3. Knowledge-based Management System
It is similar to a database management system in an information system. Its major task is
to up data the knowledge base with knowledge and rules.
4. Work Space
The workspace or black board is a memory area used for describing the current problem,
and storing intermediate results.
5. Explanation Facility
Most expert systems have explanation facilities. It explains how recommendations are
derived. The user can know how the expert system arrived at the solution, why some alternatives
were rejected, why some information was asked for etc. The explanation facility answers these
questions by referring to the system goals, data input and the decision rules. For example, in
case of loan proposal evaluation, the expert system’s explanation facility will clarify on probing
why one application was approved and why another was rejected. In case of a medical expert
system such as Mycin, this facility builds confidence in the user about the expert system and the
solution it provides to problem.
6.Reasoning Capability or Knowledge Refinement
The expert system has the capability to analyze why its solution failed or succeeded and
ways of improving its solution.
7. Inference Engine
The inference engine works like the model base in decision support system. It
manipulates a series of rules using forward chining and backward chaining techniques. In
forward chaining the inference engine poses a series of if ….then condition checking. Based on
the responses a particular solution is suggested. In backward chaining technique, the inference
engine starts with the goal and checks whether the conditions leading to that goal are present.
8. User Interface
The system provides an interface for the users to interact with the system to generate solutions.
It is similar to the dialogue facility in decision support system. The artificial intelligence
technology tries to provide a natural language interface to users.
This is wonderful blog. The information you provide is great. For more on component of expert systems, visit here..Knowledge Base and Inference Engine and User Interface
ReplyDeleteI found this blog very useful for further understanding of Expert System, most especially in the integration into the Management Information System.
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